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Article

WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

by
Luca Mainetti
*,†,
Paolo Panarese
† and
Roberto Vergallo
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2022, 22(18), 6980; https://doi.org/10.3390/s22186980
Submission received: 1 August 2022 / Revised: 4 September 2022 / Accepted: 11 September 2022 / Published: 15 September 2022
(This article belongs to the Special Issue Security of IoT-Enabled Infrastructures in Smart Cities)
Browse Figures
Figure 1
<p>Research methodology.</p> "> Figure 2
<p>WoX reference model. WoX simplifies the Application layer as it wraps the specific Presentation Objects (PO), Business Objects (BO) and Data Objects (DO) for each (OBServable) thing. By this way, it reduces the application volume.</p> "> Figure 3
<p>MOF model.</p> "> Figure 4
<p>Solution workflow.</p> "> Figure 5
<p>Example: Behavior graph after 1 day.</p> "> Figure 6
<p>Example: Behavior graph after 2 months.</p> "> Figure 7
<p>Example: Behavior graph after 4 months.</p> ">
Review Reports Versions Notes

Abstract

:
The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%.

1. Introduction

One of the expected goals of ICT services is to provide relevant information for citizens. Among the security services, such as authentication, access control, key management and intrusion detection, user authentication is very much needed for a smart city environment [1]. Strong security layers—based for example on the blockchain—do exist, providing a secure communication system in an intelligent city [2]. However, very few applications are really focused on User Experience (UX) [3]. In particular, user authentication and authorization stride with the seamless fruition of services in the smart city [4]. When taking into account such security requirements, smart city stakeholders (policy makers, engineers, architects, designers, industry, startups, etc.) not only should fulfill them but also they should find a good trade-off between security and ease of use, to deliver a perceivable value to the citizen [5].
Different sub-concepts below the main vision of smart cities exist [6] and are becoming reality: smart mobility [7,8,9], smart grid [10,11,12], and smart buildings [13,14,15] (including smart home [16,17,18,19]) are just some main umbrella terms for a rich set of services that can improve our daily life. When the access is authenticated, service fruition can be customized, paid for and obviously authorized. Allowing the users to authenticate themselves with zero friction, while not exposing the user and the systems to attackers, is an extra step that currently represents a research challenge [20,21,22]. In most cases, the requested use of a second factor can further increase the identification process complexity [23], hence erecting barriers especially for the less accustomed to technology, who may feel excluded from the evolution of the smart city.
To this aim, the adoption of smart authentication systems, such as the ones based on biometric technologies [24], can sensibly improve the UX in accessing digital services in the smart city, for example biometric solutions for access control [25,26] or wearables as tools for implicit authentication [27,28]. When an access to a service is requested, the user can simply use a fingerprint/palm/iris/voice timbre recognition system, depending on the type of smart service to be accessed. Although those technologies are quite secure, common and well accepted [29], this kind of physical biometric is more or less invasive [30], by means of quantity of attention the user should pay to use the provided interface, or the encumbrance of the equipment. Luckily, biometric is not compulsorily physical: the behavioral biometric is a choice, when unobtrusive authentication is not an option [31,32,33].
The extension of the behavior concept is the habit. Every one of us has habits, i.e., particular sequences of actions, motions and states, with a repetitive time pattern, that distinctively identify each of us among other persons. The main research question we try to answer in this paper is: if conveniently captured, could such habits be used to continuously identify the citizens along their pathway in the smart city? The smart city is the perfect environment to extend the concept of behavioral biometrics, i.e., to go over the simple retrieving of device/app usage patterns or motion patterns, by exploiting the pervasiveness of IoT sensors in different context, which allow access to a continuous stream of data comparable with previously mined habits [34,35,36]. Habits mining is crucial for the depicted scenario, because often users do not even know the habits they have, especially when the granularity on the sensing data is very fine, and it is still a research challenge.
We strongly believe that the passwordless future that is going to be standardized by the FIDO Alliance [37] will further evolve towards a zero-effort, zero-distraction and zero-encumbrance approach. There is the need to create a new research line and overcome a current research gap that is evident today in the literature, as there are no models and methods for checking the identity of the users able to not disturb their attention in any way. The motivation of the study lies in filling this gap by exploiting the habits to continuously authenticate the user in the smart city, thanks to a fuzzy check between real time user behaviors and previously automatically recorded habits. If the confidence level is over a certain threshold, then it can be supposed that the user requesting access to a specific service is indeed who s/he say s/he is.
To this aim, in this paper we use WoX+, a meta-model-driven approach to mine and match user habits exploiting the pervasiveness of sensors deployed in the smart city. It is based on a previous work, called WoX [38], which we thoroughly report in Section 2. WoX+ is a meta-model for WoX, as it defines how user interaction with the IoT can be described using the WoX model. We present a novel machine-learning (ML) block based on the WoX+ meta-model that can mine user rules (i.e., daily habits) and automatically instance them in the shape of WoX rules (so, without knowing them a priori). The independence of such rules from the physical layer, which is the main benefit of WoX, allows such rules to “follow” the user also when s/he moves all along the smart city. Therefore, when similar combinations of triggers are captured, the same security-critical reaction (like a payment) can be safely fired.
To proof the effectiveness of WoX+ as a Continuous Authentication (CA) layer in the smart city, in this paper we asked to a cohort of 10 persons to tell us a financial habit they have that they think should be captured and automatized by an IoT layer. We chose the financial context because it is ubiquitous (we can pay anywhere), it is inherently multi-device, it is rich in time patterns (purchases are repeatable in time), and most of all it requires a secure authentication. Therefore, we generated a synthesized dataset and inputted it to the ML block of WoX+, to check the capability of WoX+ to mine well known habits. In this case, results show that the Mean Squared Error Percentage (MSEP) that we reached is 0.04 (accuracy of 96%). Moreover, we asked the cohort to tell us how they expect the comprehension of the personal habit should be used to automate the person’s identification in the smart city. In this case, we analyze their answers, and we try to sketch some conclusions.
To summarize the main objectives of the study, we identify the following ones:
  • to define the requirements of a habits-based behavioral biometric system as a CA layer for the smart city
  • to define, implement and measure a ML block able to mine custom user habits from daily sensing data
  • to perform a quantitative and qualitative evaluation of the overall system
Figure 1 represent the research methodology adopted in this paper to validate and measure the effectiveness of WoX+ as a habit-based CA tool for the smart city. The interview is needed to extract the expectations from a cohort about the main system idea. Then, we use the referred use cases to list a set of generic system requirements, to check the adherence of our system to the same and consequently to perform a comparative analysis with previous studies. In parallel, we use the same use cases to generate the training data for our system and perform a quantitative evaluation.
The rest of the paper is structured as follows. Section 2 reports on the background and the authors’ previous studies, including IoT meta-models, WoX and WoX+. Section 3 describes the proposed method to mine user habits. Section 4 reports on the validation we performed. We discuss the main results of our work in Section 5. In Section 6 we summarize the conclusions and sketch future research efforts. Finally, we report the abbreviations in the back matter.

2. Background and Previous Studies

2.1. WoX

Web of Topics (WoX) is a model-driven approach for the Internet of Everything (IoE). In WoX, the crucial concept mediating between who needs and who offers IoT capabilities is the topic, which wraps the value of a feature of interest (temperature, presence, or even more abstract concepts), in a location defined similar to a URI. Moreover, WoX has the concept of Role, by which an IoT entity can declare their interest in the topic, by means of the technological (source/executor/function) and collaborative (capability/need) dimensions. WoX brings two main advantages:
  • Virtual things, beside physical things, can be easily wired up. WoX concepts are close to the people’s understanding: everyone can design and deploy custom scenarios.
  • WoX accelerates the development of applications, by taking care of the communication toward the heterogeneous IoT layer. It hides the communication protocol details, letting designers/developers concentrate on their business.
WoX is an abstraction layer placed in between the Web of Things (WoT) and the applications (Figure 2). WoX provides ready-to-use Business Objects (BO) and Data Objects (DO), leaving to the developer the only duty to display or handle the upcoming data.

2.2. IoT Meta-Models

The overall IoT meta-model can be seen as a combination of five meta-models, each of which reflects a particular view of the IoT:
  • The Human-Object-View metamodel: considers the human and the physical object both users of the IoT. To interact, a physical object must be able to hear, speak, think, inform about its being and change its being. These communication, calculation, information acquisition and activation capabilities are provided to the object by a device to which it is incorporated or connected. A physical entity can be a human or physical object.
  • The Service-View metamodel: exposes, in the form of services, the functions of information acquisition, processing and embedded actions. Services provide the basis to allow a man and a physical object to interact.
  • The Context-View metamodel: Such an interaction occurs in a context, i.e., any information useful to characterize an entity’s situation. An entity is a person, place or object that is relevant for the interaction.
  • The Network-View metamodel: The exchange, as a result of an interaction, is made on top of a communication network, which is conceptualized in the Network-view meta-model
  • The Location-View metamodel: The location of the man and/or object can affect such an exchange. This meta-model is aimed at designing both the localization of men and objects as well as their involvement in interaction.
The concept of smart home arises when the IoT is part of the residential environment. The home automation sector promotes technologies able to modify the state of the equipment and systems installed in the house, for example using the remote control possessed by the owners. The IoT overcomes the mere control and introduce an intelligent component that can automate the home management. In this way, system and devices of the smart home can work in synergy: they exchange data and information, so they are able to automatize the occupants’ actions and customize them according to their preferences and habits. The natural extension of this approach is that the system will learn from the occupants’ actions and controls so it will act proactively, hence optimize the management of the whole home environment.
Developers involved in research projects in the world of IoT have encountered problems due to the lack of standardization of a technology and an architecture for the same IoT applications. For the interaction of physical objects and intelligent applications, the current paradigm of software development, based on object orientation, has reached the height of the update and is also not suitable enough for applications and smart devices. The branch of Engineering that deals with this aspect is Model-Driven Software Development (MDSD) which marks a change in paradigm from object to model: the models do not constitute a simple documentation but are considered equivalent products and convertible into code. A new model architecture was presented in 2017 [39]. This model, called Meta-object Facility (MOF) involves four different levels.
In the MOF model (Figure 3), starting from the bottom, we have:
  • The IoT solution Implementation layer (M0), containing all the IoT devices that gather information from the real world (e.g., the temperature sensor);
  • The IoT Solution Model layer (M1), virtualizing the IoT devices from the underlying layer (e.g., WoX);
  • The IoT Meta-Model layer (M2), which generalizes the information and the interactions between the IoT layers;
  • The Meta-Object Facility or IoT Meta-Meta-Model layer (M3).

2.3. WoX+

As already discussed in [18], WoX+ is a WoX plugin that makes WoX a proactive system, based on a bottom-up configuration that uses smart rules, calculated using the historical interactions.
For the proactivity property, WoX+ uses the cold data from the WoX system to understand the pattern of the requests. It uses a ML block to generate the WoX+ Model, a schema that describes the events and the operations to run.
The cold IoT data are defined as:
d a t a : = [ { t n , a v , p v , t , d } ]
where tn is the topic name (feature + location), av is the actual value of the specific topic, pv is the preferred value, t is the time instant of the interaction and d is the specific device that send the data.
The WoX+ Model M is defined as:
M : = [ [ { t n , a v , c r } ] , [ { t n , p v } ] ]
where tn is the topic name (feature + location), av is the actual value of the specific topic, cr is the criteria of the event and pv is the preferred value.
The cr value is a comparison operation:
c r : = { = = , ! = , < , < = , > , > = }
From the implementation point of view, WoX+ is developed in Python version 3.10.4 by Paolo Panarese (one of the author of this paper, using the AWS Software Development Kit (SDK) (boto3 library, version 1.24.71, developed by Amazon) to interact with WoX and sends the IoT data to the ML block via REST APIs (using Flask version 2.2.2). This plugin is deployed in cloud.

3. Proposed Method

3.1. Mining Process

From the user’s perspective, the information flows as follow (see Figure 4):
1.
the user interacts (directly and indirectly) with the IoT system;
2.
ones per day, the IoT middleware sends all the user requests to a ML algorithm;
3.
the ML algorithm extrapolates the user behavior from the user requests;
4.
the extracted user behavior rules are sent to a rule player;
5.
the rule player waits until a rule can be activated and executes it.

3.2. Machine-Learning Block

The ML block has the purpose to understand the human behavior using the data gathered by the IoT devices. This block has the purpose to analyze the user interaction with the IoT world and generates the rules which describe the user habits.
First, we tried to model a neural network algorithm [40,41]. There are different types of neural network algorithms that we can use but, unlike what one might imagine, these algorithms are not suitable for us. There are several jobs that a neural network can do really well (classification, regression, transcription, anomaly detection, denoising, synthesis and sampling, etc.) but seems that the extraction of a habit from a list of input data is not one of them. For example, the predictive algorithms [42,43,44,45,46] are designed to predict one or more unknown value after a (single) training phase. We do not want this algorithm because we want to know (if exists) what is the unknown pattern behind the user IoT interactions, upgradeable every day.
For these reasons, we must change approach to this problem, trying to model in a different way the interaction between the human and the IoT devices.
To reach the goal we have modeled our problem using the graph theory, where the nodes are the interaction with an IoT device in a specific time slot, and the edges identify the correlation between two different nodes.
In a more mathematical way, we define the starting dataset where the IoT interactions are stored, the algorithm parameters, the graph entities and the algorithm to extrapolate the rules.

3.2.1. IoT Dataset Definition

A dataset D is a list of IoT interactions composed of:
  • sensor identifier s_id
  • date of interaction d
  • time of interaction t

3.2.2. Parameters Definition

  • Max time delay (max_t) is the max amount of time (in minutes) to consider two different nodes related;
  • Similarity Max Delay (sim_max_del) is the max amount of time (in minutes) to consider equivalent two nodes with the same id.
  • Multiplication factor (mult) is a value that scales older dataset information. It must be between 0 (ignore old values) and 1 (consider all values with the same weight).
  • Minimum rule percentage (min_rule_perc) is the threshold value of the edge to overcome to be a rule.
  • Minimum percentage (min_perc) is the threshold value below which the edge value is rounded to zero.

3.2.3. Behavior Graph Definition

Node. A node N is defined as:
n : = { s _ i d , t }
With this definition, two interactions with the same sensor and time of interaction, but in two different days, are mapped in the same node.
Interaction. We define interaction I between two devices i, j in a particular day d, such as I(i, j, d):
I ( i , j , d ) : = { 1 , if ∃ t ∈ ℜ , 0 < ε ≤ m a x _ t ∣ ∃ D ( i , t , d ) ∧ D ( j , t + ε , d ) 0 , otherwise
In a simple way, interaction I between two nodes in a particular day is 1 if the time delay between the two executions is less than or equal to max_t.
Edge. Given two nodes N1 and N2 and a specific day d, the weight of the edge between these nodes is defined as:
E ( i , j , d ) : = E ( i , j , d − 1 ) + I ( i , j , d ) 2 ∗ m u l t
Using this formula, the weight of the edge can change day by day, and consider more the last few days than the first ones.
Another optimization is to round to 0 if the weight is less than min_perc.

3.2.4. Algorithm Definition

The algorithm behind the ML block is based on 5 steps:
  • constructor (Algorithm 1): the setup of all parameters and variables used in the algorithm;
  • get_related_rows (Algorithm 2): returns the list of related nodes starting from a source node;
  • get_node (Algorithm 3): if the input node does not exists, the algorithm creates it, otherwise returns the existing node;
  • calculate_rules (Algorithm 4): the rules extractor starting by the graph generated;
  • elaborate_day (Algorithm 5): the elaboration of a subset D(d) in a specific day, defined as
    D ( d ) : = { ( s _ i d , d , t ) ⊂ D }
Algorithm 1 Constructor
1:
Setup parameters
2:
n o d e _ i d s ← [ ]
3:
n o d e s ← { }
4:
a d j _ m a t ← [ [ 0 ] ]
Algorithm 2 get_related_rows
Input: s o u r c e _ n o d e , i n p u t _ d a t a , m a x _ t
Output:  r e l a t e d _ r o w s
   1:
m i n _ t i m e ← s o u r c e _ n o d e . t
   2:
m a x _ t i m e ← m i n _ t i m e − m a x _ t
   3:
r e l a t e d _ r o w s ← [ ]
   4:
for all  r o w ∈ i n p u t _ d a t a  do
   5:
    if  r o w . s _ i d ≠ s o u r c e _ n o d e . s _ i d  then
   6:
        if  m i n _ t i m e ≤ r o w . t ≤ m a x _ t i m e  then
   7:
             r e l a t e d _ r o w s . a p p e n d ( r o w )
   8:
        end if
   9:
    end if
 10:
end for
 11:
return  r e l a t e d _ r o w s
Algorithm 3 get_node
Input:  r o w , t e m p _ a d j _ m a t
Output:  r e l a t e d _ r o w s
   1:
if  r o w . s _ i d ∈ n o d e s _ i d s  then
   2:
 Find the nearest node in n o d e s [ r o w . s _ i d ] with t <  s i m _ m a x _ d e l a y
   3:
    if  n e a r e s t _ n o d e exists then
   4:
        return  n e a r e s t _ n o d e
   5:
    end if
   6:
else
   7:
    r o w . i d ← s i z e ( n o d e s _ i d s )
   8:
   Append r o w to n o d e s [ r o w . s _ i d ] and n o d e s _ i d s
   9:
 Add 1 empty row and 1 empty column to a d j _ m a t and t e m p _ a d j _ m a t
 10:
  return  r o w
 11:
end if
Algorithm 4 calculate_rules
Input:  a d j _ m a t
Output:  r u l e s
   1:
r u l e s ← [ ]
   2:
Generate directed graph from a d j _ m a t
   3:
Remove edges with weight < m i n _ r u l e _ p e r c
   4:
Get connected components of the graph
   5:
for all  c o n n e c t e d _ c o m p o n e n t ∈ c o n n e c t e d _ c o m p o n e n t s do
   6:
    if  s i z e ( c o n n e c t e d _ c o m p o n e n t ) > 1 then
   7:
       Sort nodes of the connected component graph by time
   8:
       Append the nodes to r u l e s
   9:
    end if
 10:
end for
 11:
return  r u l e s
Algorithm 5 elaborate_day
Input:  i n p u t _ d a t a
Output:  r u l e s
   1:
t e m p _ a d j ← NxN zero matrix with N = size(nodes_ids)
   2:
for all  s o u r c e _ r o w ∈ i n p u t _ d a t a
   3:
     s o u r c e _ n o d e ← g e t _ n o d e ( s o u r c e _ r o w , t e m p _ a d j )
   4:
     r e l a t e d _ r o w s ← g e t _ r e l a t e d _ r o w s ( s o u r c e _ n o d e , i n p u t _ d a t a , m a x _ t )
   5:
    for all  r e l a t e d _ r o w ∈ r e l a t e d _ r o w s do
   6:
         r e l a t e d _ n o d e ← g e t _ n o d e ( r e l a t e d _ r o w , t e m p _ a d j )
   7:
         t e m p _ a d j [ s o u r c e _ n o d e . s _ i d ] [ r e l a t e d _ n o d e . s _ i d ] = 1
   8:
    end for
   9:
end for
 10:
a d j _ m a t ← ( a d j _ m a t + t e m p _ a d j ) / 2 ∗ m u l t
 11:
Rounds to 0 the a d j _ m a t values less than m i n _ p e r c
 12:
r u l e s ← c a l c u l a t e _ r u l e s ( a d j _ m a t )
 13:
return  r u l e s

3.3. Habits-Based Continuous Authentication Requirements

Analyzing the answers given by the cohort, we can summarize as follows the requirements of a habit-based continuous authentication system for the smart city:
1.
a sensed value should be independent from the specific device that generates the reading
2.
mined habits should be identifiable over different physical setups
3.
a habits-matching layer should be flexible enough to recognize with a certain precision a typical habit even if not all the exact conditions occur
4.
authorization-based services should be informed about the opening (or closing) of a secure session for a specific user, fired by the detection of a habit
5.
both temporal and spatial information should be provided to open a contextual secure session in time and space
6.
the user should tell authorization-based services who s/he claims to be, prior to use the service in frictionless mode
7.
the way the user claims to be himself should be constant among the different auth-based scenarios
8.
the way the user claims to be himself should be independent from the media used (i.e., mobile-based BLE or WiFi, smartcard)
9.
a spatio-temporal matching engine should fuzzy-match different units of time (e.g., weekdays, months, a nth part of the month, seasons) and taxonomies of locations, both hierarchical (e.g., town-city-province-state) and flat (e.g., beaches, parking lot)

4. Functional and Quantitative Validation

To validate the algorithm, we have searched the Internet for a dataset of IoT interactions with the habit description. This information would be used to train the algorithm and measure the results. Unfortunately, we did not find this particular information and, for this reason, we preferred to generate a synthetic dataset by interviewing a cohort, as described in the next section.

4.1. Scenario

The experiment is aimed at clarifying how WoX+ could be used as a continuous authentication layer in the smart city, particularly in the financial context.
The 10 persons who took part in this experiment where workers in the IT (five developers, four entrepreneurs, one clerk), proportion between male and female is 80% vs. 20%, and the average age is 32. We decided to involve only very skilled persons because of the complexity in understanding our requests.
In Table 1 we describe what we collected from the expectation of each participant. To populate the 2nd to 4th column, we asked them the following questions, leaving them one business day to give us the answers:
1.
What is a spending habit you have that you think an IoT layer could capture?
2.
What is a different context that you expect such an intelligent IoT layer should match with the spending habit you described, to automatize the payment?
3.
What is a security-related scenario that could exploit the occurrence of your habit to provide a frictionless experience?

4.2. Synthetic Data Generation

We followed these steps:
  • We have defined a set of rules R;
  • We have generated a dataset with the interactions defined in the rules and a random-generated noise interactions;
  • We have trained the algorithm and we have taken the resulting rules;
  • We have compared these results with R.
In particular we have generated the dataset using a set of meta-parameters:
  • action_delta_minutes is the delay time (in minutes) between two actions in the same rule;
  • action_probability is the probability to generate an action to the dataset;
  • noise_sensors is the number of random interactions to generate;
  • noise_occurrences is the number of occurrences of a noise sensor for each day;
  • noise_probability is the probability to generate a noise sensor;
  • time_scale is the variance of the Gaussian function used to generate the interaction instant.
Given a rule r ⊂ M , for a particular action a and for each day, the interaction instant t is generated using the formula:
t = t r ( M ) + n o r m ( μ = 0 , σ 2 = t i m e _ s c a l e )
where t r ( M ) is the start time of the rule r and n o r m is a function that generates a random Gaussian distributed variable that defines the offset from the rule (in seconds). We can define the first and third quartile starting from the t i m e _ s c a l e value:
Q 1 : = μ − 0.6745 ∗ σ
Q 3 : = μ + 0.6745 ∗ σ
An example of offset probability distribution can be found at Table 2.
For example, with a t i m e _ s c a l e = 100, we have 50% of chance (definition of IQR) to generate a time in a range of 2 min and 15 s (≈135 s) from the rule starting time, and 68.27% of chance to generate in a range of 3 min and 10 s (200 s).
For each meta-parameter, we define a set of values that we want to test for the specific meta-parameter. In particular, we tested the algorithm with:
  • a c t i o n _ d e l t a _ m i n u t e s : 1, 2 and 4 min;
  • a c t i o n _ p r o b a b i l i t y : 90%, 95%;
  • n o i s e _ s e n s o r s : 5, 10, 20;
  • n o i s e _ o c c u r r e n c e s : 3, 5;
  • n o i s e _ p r o b a b i l i t y 30%, 50%, 70%.
  • t i m e _ s c a l e : 500.
With these values, we generate a total of 10 ∗ 108 = 1080 datasets.
The graph algorithm parameters that we define are the following:
  • m a x _ t : 300 min;
  • s i m _ m a x _ d e l : 24 h;
  • m u l t : 0.95;
  • m i n _ r u l e _ p e r c : 0.8;
  • m i n _ p e r c : 0.25.

Sample Behavior Graph Visualization

When the IoT cold data are sent to the ML block, the behavior graph is generated (or updated). An example of behavior graph applied to an experimental scenario is the following: Every 2 months, when the decalcification warning light on the coffee machine turns on, I buy the decalcifier on Amazon (habit ID 1).
The behavior graph after 1 day is the following (Figure 5):
As we can see, the nodes are generated correctly based on the sensor name. In particular:
  • decalc_warn is the IoT signal that the coffee machine needs the decalcifier;
  • buy_decalc is the IoT signal that the user buys the decalcifier;
  • interaction_1 is a noise sensor that is not involved in this particular behavior.
All the edges are dashed and gray, based on the value of the adjacency matrix.
After 2 months the graph (Figure 6) remains the same, except for the color of the edges, indicates that the adjacency matrix value is growing, and for adding another noise interaction (interaction_2).
After 4 months from the first day, the graph (Figure 7) change and the edges become greater than the m i n _ r u l e _ p e r c (0.8 for this example). The algorithm creates a graph with all the nodes with an edge greater than the m i n _ r u l e _ p e r c value, divides the graph in N subgraph, one for every connected component, and calculates the largest path between the first node (in time) and the last one (and colors this path with red).
For this example, the extracted rule is:
{
   "triggers": [
      { "tn": "decalc\_warn", "av": "TRUE", "cr", "=" }
   ],
   "actions": [
      { "tn": "buy\_decalc", "pv": "TRUE" }
   ]
 }

4.3. Habits Mining Validation Definition and Results

We have run the algorithm with all the 1080 datasets, and we calculated the score for each rule. Given the original set of rules R 0 ⊂ R and the set of calculated rules R c ⊂ R , the score for each calculated rule r c is calculated with the following schema:
  • we find, if exists, a rule r 0 ⊂ R 0 similar to r c ;
  • if r 0 does not exists, the score is 0;
  • if the trigger of r c is equal to the trigger of r 0 , the score starts from 2;
  • if the trigger of r c is different to the trigger of r 0 but appears into the list of actions of r 0 , the score starts from 1;
  • if the trigger of r c is different to the trigger of r 0 but appears into the list of actions of r 0 with different starting time, the score starts from 0.5;
  • for each action of r c , if it exists in the list of actions of r 0 , we add 0.5 to the score.
The total score is calculated:
m a x _ s c o r e = 2 + 0.5 ∗ # a 0
where # a o is the size of the list of actions of r 0 .
For each dataset, the MSEP is evaluated as follows:
M S E P = ( m a x _ s c o r e − s c o r e m a x _ s c o r e ) 2
The training and evaluation phases of our algorithm were performed on a local machine with the following features: MacBook Pro 15-inch (2016), Intel Core i7-6820HQ 2.70 GHz as the CPU, 16 Gb of RAM. After the test execution, the MSEP that we reached with the 1080 datasets is 0.04 (accuracy of 96%).
To better analyze the result, we calculated an MSEP for each value of each parameter. We found that no properties affect the result, as the MSEP remains stable for different dataset and parameters.

5. Discussion

In Table 3 we report the requirement that we listed in Section 3.3, demonstrating whether our approach satisfies or not each of this, with a brief comment on the reason.
We can now perform a comparison between the proposed approach with others falling in the broader field of biometric-based continuous authentication. Ref. [48] is a recent (2021) review in which three categories of continuous authentication are taken into account: physiological-based (such as fingerprint, iris, voice, face), behavioral-based (such as keystroke dynamics, touch dynamics, motion dynamics, etc.), and context-aware factors (such as physical location, IP-addresses, device-specific data, browsing history, etc.).
Table 4 report the list of biometric technologies identified in [48] and useful to perform continuous authentication. A specific comment is placed for each category, to discuss the comparison with our work.

Performance Discussion

To test the technical performance of the machine learning algorithm, we tested the system with different datasets with a single date. This test simulates the daily IoT data sent from the WoX+ module to generate the user habits rules. The datasets differ from each other by the quantity of different row stored inside them. We tested using 5, 10, 15, 20, 30 and 50 sensors and 5, 10, 20, 50 interactions and the results are shown below (Table 5).
We analyzed the results of these tests and found that the number of sensors in a dataset does not affect the mining time. However, the number of interactions in a dataset (equals to the number of dataset rows) affects the time spent to execute a single-day algorithm. This difference is caused by the nature of the graph algorithm: the graph with N nodes is defined as a matrix of size N × N . For this reason, increasing N, cause a quadratic increase in the execution time. The experimental increasing is not quadratic thanks to two optimizations: the m i n _ p e r c removes all the interactions with low probability and, if a row is full of zeros, the algorithm removes it, reducing the size of the matrix.

6. Conclusions

In this paper, we have presented WoX+, a meta-model for the IoT. It is the evolution of WoX, a previous work of the authors. WoX is a model-driven approach for the IoT, allowing the definition of top-down IoT rules using very simple high-level concepts that any stakeholder can understand. The defect of WoX is that IoT rules, defined by triggers (i.e., relations between topic values) and reactions, must be known a priori. We expect instead that smart environments should automatically detect rules from the evidence given by IoT cold data. Therefore, we created WoX+, which is the meta-model for WoX. WoX+ can mine rules from previously recorded data and instance automatically such rules. In the smart city, such rules can represent customized user habits, i.e., particular sequences of actions, motions and states, with a repetitive time pattern that distinctively identify each of us among other persons. If conveniently captured, such habits can be used to continuously identify us along our pathway in the smart city.
To investigate this possibility, we asked 10 persons to give us a habit they have involving digital payments. The choice of the payments domain is due to the need for such use cases to be authorized. We modeled the 10 habits using WoX concepts and manually generated the rules, then we used such rules to generate a synthesized dataset. We used the dataset to feed the ML block of WoX+ which successfully reconstructed and instanced the rules. In particular, we gave a score based on how much similar they are, and we divided the score with the maximum possible score. Results show that the error in reconstructing the rules is acceptable (MSEP 0.04%). Moreover, we also asked the cohort to suggest a similar payment scenario they expect that a smart environment should automatically authorize because of the previously recorded habit. By replying to all the questions, the cohort confirmed that a continuous authentication layer based on habit-base behavioral biometrics can be effective to enhance the security-UX trade-off in the smart city.
More in general, we discovered that WoX+’s meta-model-driven approach led to two main benefits to better trade-off security and UX aspects in the smart city:
  • the first is that mining user habits can automatize actions related to security aspects;
  • the second is that the occurrence of the habit can be used as a proof of the user’s identity, and then unlock the frictionless fruition of secured services in the smart city.
The algorithm we have described is at the early stage of development, so it has relevant limits. We identified such technical limitations:
  • there is no difference between weekdays and holidays;
  • the system is not able to find periodical events or seasonal behavior;
  • the system generates rules strictly related to the datetime information;
These limitations reduce the number of habits that the algorithm can discover: for example, we must specify the largest time period (in the experimental cases is 2 months) after which an event must be re-executed to become a habit. Choosing a lower value may cause the loss of some habits with greater periodicity; however, a higher value will cause the necessary data increment (and a large period) to find the habits. In the experiment, we considered the greatest time period value before executing the training phase.
From a technical point of view, next research efforts involve the improvement of the similarity between two actions: we want to better understand the link between a rule extracted by the ML block and the current action of the user. This improvement will simplify the user authentication.
With regards to the validation of the approach, a new experiment is needed. In this paper, we have generated a synthesized dataset, because of the difficulty to find suitable dataset on the Internet. The next experiment will generate this dataset by sensing the IoT, hence capturing the habits using real data.
Although listening to user expectations is useful to understand the requirements of a behavioral biometrics system, future research efforts must include the implementation of the 10 continuous authentication expectations of the cohort, to check and measure on the field the capability of WoX+ in meeting user expectations.

Author Contributions

Supervision, L.M.; Formal Analysis, P.P.; Conceptualization, R.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the 10 persons who took part in the experiment involving the WoX+ model.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
dataIoT data sent to the system
MWoX+ Model
crWoX+ trigger criteria
s_idSensor identifier
dInteraction date
tInteraction time
max_tMax time delay
sim_max_delSImilarity Max Delay
multMultiplication factor
min_rule_percMinimum rule percentage
min_percMinimum percentage
nNode
I(i, j, d)Interaction
E(i, j, d)Edge
D(d)Subset of dataset with date as d

References

  1. Bera, B.; Das, A.K.; Balzano, W.; Medaglia, C.M. On the design of biometric-based user authentication protocol in smart city environment. Pattern Recognit. Lett. 2020, 138, 439–446. [Google Scholar] [CrossRef]
  2. Chinnasamy, P.; Vinothini, C.; Arun Kumar, S.; Allwyn Sundarraj, A.; Annlin Jeba, S.; Praveena, V. Blockchain Technology in Smart-Cities. In Blockchain Technology: Applications and Challenges; Springer: Berlin/Heidelberg, Germany, 2021; pp. 179–200. [Google Scholar]
  3. Oliveira, T.A.; Oliver, M.; Ramalhinho, H. Challenges for connecting citizens and smart cities: ICT, e-governance and blockchain. Sustainability 2020, 12, 2926. [Google Scholar] [CrossRef]
  4. Distante, C.; Fineo, L.; Mainetti, L.; Manco, L.; Taccardi, B.; Roberto, V. HF-SCA: Hands Free Strong Customer Authentication based on a memory-guided attention mechanisms. J. Risk Financ. Manag. 2022, 15, 342. [Google Scholar] [CrossRef]
  5. Belanche-Gracia, D.; Casaló-Ariño, L.V.; Pérez-Rueda, A. Determinants of multi-service smartcard success for smart cities development: A study based on citizens’ privacy and security perceptions. Gov. Inf. Q. 2015, 32, 154–163. [Google Scholar] [CrossRef]
  6. Kadłubek, M.; Thalassinos, E.; Domagała, J.; Grabowska, S.; Saniuk, S. Intelligent Transportation System Applications and Logistics Resources for Logistics Customer Service in Road Freight Transport Enterprises. Energies 2022, 15, 4668. [Google Scholar] [CrossRef]
  7. Paiva, S.; Ahad, M.A.; Tripathi, G.; Feroz, N.; Casalino, G. Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges. Sensors 2021, 21, 2143. [Google Scholar] [CrossRef]
  8. Butler, L.; Yigitcanlar, T.; Paz, A. How can smart mobility innovations alleviate transportation disadvantage? Assembling a conceptual framework through a systematic review. Appl. Sci. 2020, 10, 6306. [Google Scholar] [CrossRef]
  9. Maldonado Silveira Alonso Munhoz, P.A.; da Costa Dias, F.; Kowal Chinelli, C.; Azevedo Guedes, A.L.; Neves dos Santos, J.A.; da Silveira e Silva, W.; Pereira Soares, C.A. Smart mobility: The main drivers for increasing the intelligence of urban mobility. Sustainability 2020, 12, 10675. [Google Scholar] [CrossRef]
  10. Fachechi, A.; Mainetti, L.; Palano, L.; Patrono, L.; Stefanizzi, M.L.; Vergallo, R.; Chu, P.; Gadh, R. A new vehicle-to-grid system for battery charging exploiting IoT protocols. In Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain, 17–19 March 2015; pp. 2154–2159. [Google Scholar]
  11. Ghasempour, A. Internet of things in smart grid: Architecture, applications, services, key technologies, and challenges. Inventions 2019, 4, 22. [Google Scholar] [CrossRef]
  12. Espe, E.; Potdar, V.; Chang, E. Prosumer communities and relationships in smart grids: A literature review, evolution and future directions. Energies 2018, 11, 2528. [Google Scholar] [CrossRef] [Green Version]
  13. Buckman, A.H.; Mayfield, M.; Beck, S.B. What is a smart building? Smart Sustain. Built Environ. 2014, 3, 92–109. [Google Scholar] [CrossRef]
  14. Apanaviciene, R.; Vanagas, A.; Fokaides, P.A. Smart building integration into a smart city (SBISC): Development of a new evaluation framework. Energies 2020, 13, 2190. [Google Scholar] [CrossRef]
  15. Verma, A.; Prakash, S.; Srivastava, V.; Kumar, A.; Mukhopadhyay, S.C. Sensing, controlling, and IoT infrastructure in smart building: A review. IEEE Sens. J. 2019, 19, 9036–9046. [Google Scholar] [CrossRef]
  16. Lobaccaro, G.; Carlucci, S.; Löfström, E. A review of systems and technologies for smart homes and smart grids. Energies 2016, 9, 348. [Google Scholar] [CrossRef]
  17. Capodieci, A.; Mainetti, L.; Panarese, P. Ambient Assisted Living for Elderly People Using Smart Personal Assistants. In Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 12–14 December 2018; pp. 935–940. [Google Scholar]
  18. Panarese, P.; Meraglia, E.; Vergallo, R.; Mainetti, L. Enhancing Voice Assistants: A Proactive Approach. In Proceedings of the 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech), Bol and Split, Croatia, 8–11 September 2021; pp. 1–4. [Google Scholar]
  19. Mainetti, L.; Manco, L.; Patrono, L.; Secco, A.; Sergi, I.; Vergallo, R. An ambient assisted living system for elderly assistance applications. In Proceedings of the 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Valencia, Spain, 4–8 September 2016; pp. 1–6. [Google Scholar]
  20. Rimmer, V.; Preuveneers, D.; Joosen, W.; Mustafa, M.A.; Abidin, A.; Rúa, E.A. Frictionless authentication systems: Emerging trends, Research challenges and opportunities. arXiv 2018, arXiv:1802.07233. [Google Scholar]
  21. Marmion, V. Exploring Identity Assurance as a Complex System. Ph.D. Thesis, University of Southampton, Southampton, UK, 2021. [Google Scholar]
  22. Xiao, Y.; Varvello, M. FIAT: Frictionless authentication of IoT traffic. In Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies, Virtual Event, Germany, 7–10 December 2021; pp. 483–484. [Google Scholar]
  23. Sacaleanu, R.; Tak, E. PSD2 Secure Customer Authentication and customer experience: Ensuring a positive impact. J. Digit. Bank. 2020, 5, 146–154. [Google Scholar]
  24. Bhattacharyya, D.; Ranjan, R.; Alisherov, F.; Choi, M. Biometric authentication: A review. Int. J. U E Serv. Sci. Technol. 2009, 2, 13–28. [Google Scholar]
  25. Manimuthu, A.; Dharshini, V.; Zografopoulos, I.; Priyan, M.; Konstantinou, C. Contactless technologies for smart cities: Big data, IoT, and cloud infrastructures. SN Comput. Sci. 2021, 2, 1–24. [Google Scholar] [CrossRef]
  26. Preuveneers, D.; Joosen, W. SmartAuth: Dynamic context fingerprinting for continuous user authentication. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, Salamanca, Spain, 13–17 April 2015; pp. 2185–2191. [Google Scholar]
  27. Ekiz, D.; Can, Y.S.; Dardagan, Y.C.; Ersoy, C. Can a smartband be used for continuous implicit authentication in real life. IEEE Access 2020, 8, 59402–59411. [Google Scholar] [CrossRef]
  28. Zhang, Y.; Hu, W.; Xu, W.; Chou, C.T.; Hu, J. Continuous authentication using eye movement response of implicit visual stimuli. Proc. ACM Interact. Mobile Wearable Ubiquitous Technol. 2018, 1, 1–22. [Google Scholar] [CrossRef]
  29. James, T.; Pirim, T.; Boswell, K.; Reithel, B.; Barkhi, R. Determining the intention to use biometric devices: An application and extension of the technology acceptance model. J. Organ. End User Comput. (JOEUC) 2006, 18, 1–24. [Google Scholar] [CrossRef]
  30. Ho, G.; Stephens, G.; Jamieson, R. Biometric Authentication Adoption Issues 2003. Available online: https://aisel.aisnet.org/acis2003/11/ (accessed on 31 July 2022).
  31. Mahfouz, A.; Mahmoud, T.M.; Eldin, A.S. A survey on behavioral biometric authentication on smartphones. J. Inf. Secur. Appl. 2017, 37, 28–37. [Google Scholar] [CrossRef]
  32. Sitová, Z.; Šeděnka, J.; Yang, Q.; Peng, G.; Zhou, G.; Gasti, P.; Balagani, K.S. HMOG: New behavioral biometric features for continuous authentication of smartphone users. IEEE Trans. Inf. Forensics Secur. 2015, 11, 877–892. [Google Scholar] [CrossRef]
  33. Liang, Y.; Samtani, S.; Guo, B.; Yu, Z. Behavioral biometrics for continuous authentication in the internet-of-things era: An artificial intelligence perspective. IEEE Internet Things J. 2020, 7, 9128–9143. [Google Scholar] [CrossRef]
  34. Cao, H.; Bao, T.; Yang, Q.; Chen, E.; Tian, J. An effective approach for mining mobile user habits. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada, 26–30 October 2010; pp. 1677–1680. [Google Scholar]
  35. Ma, H.; Cao, H.; Yang, Q.; Chen, E.; Tian, J. A habit mining approach for discovering similar mobile users. In Proceedings of the 21st International Conference on World Wide Web, Lyon, France, 16–20 April 2012; pp. 231–240. [Google Scholar]
  36. Dimaggio, M.; Leotta, F.; Mecella, M.; Sora, D. Process-based habit mining: Experiments and techniques. In Proceedings of the 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 18–21 July 2016; pp. 145–152. [Google Scholar]
  37. Machani, S.; Field, N. Choosing FIDO Authenticators for Enterprise Use Cases. In FIDO Alliance White Paper; FIDO ALLIANCE: Wakefield, MA, USA, 2022. [Google Scholar]
  38. Caione, A.; Fiore, A.; Mainetti, L.; Manco, L.; Vergallo, R. WoX: Model-driven development of web of things applications. In Managing the Web of Things; Elsevier: Amsterdam, The Netherlands, 2017; pp. 357–387. [Google Scholar]
  39. Ben Hassine, T.; Khayati, O.; Ben Ghezala, H. An IoT domain meta-model and an approach to software development of IoT solutions. In Proceedings of the 2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), Gafsa, Tunisia, 20–22 October 2017; pp. 32–37. [Google Scholar] [CrossRef]
  40. Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: Cambridge, MA, USA, 2016; Available online: http://www.deeplearningbook.org (accessed on 31 July 2022).
  41. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning; Springer Series in Statistics; Springer New York Inc.: New York, NY, USA, 2001. [Google Scholar]
  42. Wang, C.; Xiong, R.; Tian, J.; Lu, J.; Zhang, C. Rapid ultracapacitor life prediction with a convolutional neural network. Appl. Energy 2022, 305, 117819. [Google Scholar] [CrossRef]
  43. Wang, S.; Ren, P.; Takyi-Aninakwa, P.; Jin, S.; Fernandez, C. A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries. Energies 2022, 15, 5053. [Google Scholar] [CrossRef]
  44. Cho, J.H.; Moon, J.W. Integrated artificial neural network prediction model of indoor environmental quality in a school building. J. Clean. Prod. 2022, 344, 131083. [Google Scholar] [CrossRef]
  45. Ishfaque, M.; Dai, Q.; Haq, N.U.; Jadoon, K.; Shahzad, S.M.; Janjuhah, H.T. Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan. Energies 2022, 15, 3123. [Google Scholar] [CrossRef]
  46. Cheng, Y.; Wang, C.; Wu, J.; Zhu, H.; Lee, C. Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes. Appl. Soft Comput. 2022, 118, 108507. [Google Scholar] [CrossRef]
  47. Melo, N.; Lee, J.; Suzuki, R. Identification of the User’s Habits based on Activity Information. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1–5 October 2018; pp. 2014–2019. [Google Scholar] [CrossRef]
  48. Baig, A.F.; Eskeland, S. Security, privacy, and usability in continuous authentication: A survey. Sensors 2021, 21, 5967. [Google Scholar] [CrossRef]
  49. Abeni, P.; Baltatu, M.; D’Alessandro, R. Nis03-4: Implementing biometrics-based authentication for mobile devices. In Proceedings of the IEEE Globecom 2006, San Francisco, CA, USA, 27 November 2006–1 December 2006. [Google Scholar]
  50. Crouse, D.; Han, H.; Chandra, D.; Barbello, B.; Jain, A. Continuous authentication of mobile user: Fusion of face image and inertial Measurement Unit data. In Proceedings of the 2015 International Conference on Biometrics (ICB), Phuket, Thailand, 19–22 May 2015; pp. 135–142. [Google Scholar] [CrossRef]
  51. Hadid, A.; Heikkilä, J.; Silven, O.; Pietikäinen, M. Face and eye detection for person authentication in mobile phones. In Proceedings of the 2007 First ACM/IEEE International Conference on Distributed Smart Cameras, Vienna, Austria, 25–28 September 2007; pp. 101–108. [Google Scholar] [CrossRef]
  52. Samangouei, P.; Patel, V.; Chellappa, R. Facial attributes for active authentication on mobile devices. Image Vis. Comput. 2017, 58, 181–192. [Google Scholar] [CrossRef]
  53. Perera, P.; Patel, V. Face-based multiple user active authentication on mobile devices. IEEE Trans. Inf. Forensics Secur. 2019, 14, 1240–1250. [Google Scholar] [CrossRef]
  54. Kudinov, A.; Elsakov, S. Improved continuous authentication system with counterfeit protection. J. Comput. Eng. Math 2019, 6, 35–47. [Google Scholar] [CrossRef]
  55. Feng, H.; Fawaz, K.; Shin, K. Continuous authentication for voice assistants. In Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking (MobiCom ’17), Snowbird, UT, USA, 16–20 October 2017; Volume Part F131210, pp. 343–355. [Google Scholar] [CrossRef]
  56. Miguel-Hurtado, O.; Blanco-Gonzalo, R.; Guest, R.; Lunerti, C. Interaction evaluation of a mobile voice authentication system. In Proceedings of the 2016 IEEE International Carnahan Conference on Security Technology (ICCST), Orlando, FL, USA, 24–27 October 2016. [Google Scholar] [CrossRef]
  57. Zhang, L.; Tan, S.; Yang, J. Hearing Your Voice is Not Enough: An Articulatory gesture based liveness detection for voice authentication. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS ’17), Dallas, TX, USA, 30 October–3 November 2017; pp. 57–71. [Google Scholar] [CrossRef]
  58. Yan, Z.; Zhao, S. A Usable Authentication System Based on Personal Voice Challenge. In Proceedings of the 2016 International Conference on Advanced Cloud and Big Data (CBD), Chengdu, China, 13–16 August 2016; pp. 194–199. [Google Scholar] [CrossRef]
  59. Zhang, L.; Tan, S.; Yang, J.; Chen, Y. VoiceLive: A phoneme localization based liveness detection for voice authentication on smartphones. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security 2016 (CCS ’16), Vienna, Austria, 24–28 October 2016; pp. 1080–1091. [Google Scholar] [CrossRef]
  60. Nakanishi, I.; Baba, S.; Miyamoto, C. EEG based biometric authentication using new spectral features. In Proceedings of the 2009 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Kanazawa, Japan, 7–9 January 2009; pp. 651–654. [Google Scholar] [CrossRef] [Green Version]
  61. Miyamoto, C.; Baba, S.; Nakanishi, I. Biometric person authentication using new spectral features of electroencephalogram (EEG). In Proceedings of the 2008 International Symposium on Intelligent Signal Processing and Communications Systems, Bangkok, Thailand, 8–11 February 2009. [Google Scholar] [CrossRef]
  62. Das, R.; Maiorana, E.; Campisi, P. EEG Biometrics Using Visual Stimuli: A Longitudinal Study. IEEE Signal Process. Lett. 2016, 23, 341–345. [Google Scholar] [CrossRef]
  63. Louis, W.; Komeili, M.; Hatzinakos, D. Continuous authentication using One-Dimensional Multi-Resolution Local Binary Patterns (1DMRLBP) in ECG biometrics. IEEE Trans. Inf. Forensics Secur. 2016, 11, 2818–2832. [Google Scholar] [CrossRef]
  64. Coutinho, D.; Fred, A.; Figueiredo, M. ECG-based continuous authentication system using adaptive string matching. In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, Rome, Italy, 26–29 January 2011; pp. 354–359. [Google Scholar]
  65. Camara, C.; Peris-Lopez, P.; Gonzalez-Manzano, L.; Tapiador, J. Real-time electrocardiogram streams for continuous authentication. Appl. Soft Comput. J. 2018, 68, 784–794. [Google Scholar] [CrossRef]
  66. Song, C.; Wang, A.; Ren, K.; Xu, W. EyeVeri: A secure and usable approach for smartphone user authentication. In Proceedings of the IEEE INFOCOM 2016—The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA, 10–14 April 2016. [Google Scholar] [CrossRef]
  67. Zhang, F.; Zhang, D.; Xiong, J.; Wang, H.; Niu, K.; Jin, B.; Wang, Y. From fresnel diffraction model to fine-grained human respiration sensing with commodity wi-fi devices. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp ’18), Singapore, 8–12 October 2018. [Google Scholar]
  68. Eberz, S.; Lovisotto, G.; Rasmussen, K.; Lenders, V.; Martinovic, I. 28 blinks later: Tackling practical challenges of eye movement biometrics. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS ’19), London, UK, 11–15 November 2019; pp. 1187–1199. [Google Scholar] [CrossRef]
  69. Ehatisham-ul Haq, M.; Awais Azam, M.; Naeem, U.; Amin, Y.; Loo, J. Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing. J. Netw. Comput. Appl. 2018, 109, 24–35. [Google Scholar] [CrossRef]
  70. Saied, M.; Elshenawy, A.; Ezz, M. A Novel Approach for Improving Dynamic Biometric Authentication and Verification of Human Using Eye Blinking Movement. Wirel. Pers. Commun. 2020, 115, 859–876. [Google Scholar] [CrossRef]
  71. Mosenia, A.; Sur-Kolay, S.; Raghunathan, A.; Jha, N. CABA: Continuous Authentication Based on BioAura. IEEE Trans. Comput. 2017, 66, 759–772. [Google Scholar] [CrossRef]
  72. Zhang, X.; Yao, L.; Huang, C.; Gu, T.; Yang, Z.; Liu, Y. DeepKey: An EEG and Gait Based Dual-Authentication System. arXiv 2017, arXiv:1706.01606. [Google Scholar]
  73. Barra, S.; Casanova, A.; Fraschini, M.; Nappi, M. Fusion of physiological measures for multimodal biometric systems. Multimed. Tools Appl. 2017, 76, 4835–4847. [Google Scholar] [CrossRef]
  74. Sim, T.; Zhang, S.; Janakiraman, R.; Kumar, S. Continuous verification using multimodal biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29, 687–700. [Google Scholar] [CrossRef] [PubMed]
  75. Su, F.; Xia, L.; Cai, A.; Ma, J. A dual-biometric-modality identification system based on fingerprint and EEG. In Proceedings of the 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), Washington, DC, USA, 27–29 September 2010. [Google Scholar] [CrossRef]
  76. McCool, C.; Marcel, S.; Hadid, A.; Pietikäinen, M.; Matějka, P.; Černocký, J.; Poh, N.; Kittler, J.; Larcher, A.; Lévy, C.; et al. Bi-modal person recognition on a mobile phone: Using mobile phone data. In Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, Melbourne, VIC, Australia, 9–13 July 2012; pp. 635–640. [Google Scholar] [CrossRef]
  77. Abo-Zahhad, M.; Ahmed, S.; Abbas, S. A new multi-level approach to EEG based human authentication using eye blinking. Pattern Recognit. Lett. 2016, 82, 216–225. [Google Scholar] [CrossRef]
  78. Wang, M.; Abbass, H.; Hu, J. Continuous authentication using EEG and face images for trusted autonomous systems. In Proceedings of the 2016 14th Annual Conference on Privacy, Security and Trust (PST), Auckland, New Zealand, 12–14 December 2016; pp. 368–375. [Google Scholar] [CrossRef]
  79. Derawi, M.; Nickely, C.; Bours, P.; Busch, C. Unobtrusive user-authentication on mobile phones using biometric gait recognition. In Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Darmstadt, Germany, 15–17 October 2010; pp. 306–311. [Google Scholar] [CrossRef]
  80. Mäntyjärvi, J.; Lindholm, M.; Vildjiounaite, E.; Mäkelä, S.M.; Ailisto, H. Identifying users of portable devices from gait pattern with accelerometers. In Proceedings of the (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, Philadelphia, PA, USA, 23–23 March 2005; Volume II, pp. II973–II976. [Google Scholar] [CrossRef]
  81. Gafurov, D.; Snekkenes, E. Gait recognition using wearable motion recording sensors. Eurasip J. Adv. Signal Process. 2009, 2009, 415817. [Google Scholar] [CrossRef]
  82. Hoang, T.; Nguyen, T.; Luong, C.; Do, S.; Choi, D. Adaptive cross-device gait recognition using a mobile accelerometer. J. Inf. Process. Syst. 2013, 9, 333–348. [Google Scholar] [CrossRef]
  83. Muaaz, M.; Mayrhofer, R. An analysis of different approaches to gait recognition using cell phone based accelerometers. In Proceedings of the International Conference on Advances in Mobile Computing & Multimedia (MoMM ’13), Vienna, Austria, 2–4 December 2013; pp. 293–300. [Google Scholar] [CrossRef]
  84. Wu, Z.; Huang, Y.; Wang, L.; Wang, X.; Tan, T. A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 209–226. [Google Scholar] [CrossRef]
  85. Nickel, C.; Derawi, M.; Bours, P.; Busch, C. Scenario test of accelerometer-based biometric gait recognition. In Proceedings of the 2011 Third International Workshop on Security and Communication Networks (IWSCN), Gjovik, Norway, 18–20 May 2011; pp. 15–21. [Google Scholar] [CrossRef]
  86. Sun, Y.; Lo, B. An Artificial Neural Network Framework for Gait-Based Biometrics. IEEE J. Biomed. Health Inform. 2019, 23, 987–998. [Google Scholar] [CrossRef]
  87. Zhong, Y.; Deng, Y. Sensor orientation invariant mobile gait biometrics. In Proceedings of the IEEE International Joint Conference on Biometrics, Clearwater, FL, USA, 29 September 2014–2 October 2014. [Google Scholar] [CrossRef]
  88. Zhong, Y.; Deng, Y.; Meltzner, G. Pace independent mobile gait biometrics. In Proceedings of the 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), Arlington, VA, USA, 8–11 September 2015. [Google Scholar] [CrossRef]
  89. Sae-Bae, N.; Ahmed, K.; Isbister, K.; Memon, N. Biometric-rich gestures: A novel approach to authentication on multi-touch devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’12), Austin, TX, USA, 5–10 May 2012; pp. 977–986. [Google Scholar] [CrossRef]
  90. Rauen, Z.; Anjomshoa, F.; Kantarci, B. Gesture and Sociability-based Continuous Authentication on Smart Mobile Devices. In Proceedings of the 16th ACM International Symposium on Mobility Management and Wireless Access (MobiWac’18); ACM: New York, NY, USA, 2018; pp. 51–58. [Google Scholar] [CrossRef]
  91. Govindarajan, S.; Gasti, P.; Balagani, K. Secure privacy-preserving protocols for outsourcing continuous authentication of smartphone users with touch data. In Proceedings of the 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), Arlington, VA, USA, 29 September 2013–2 October 2013. [Google Scholar] [CrossRef]
  92. Zhao, X.; Feng, T.; Shi, W.; Kakadiaris, I. Mobile user authentication using statistical touch dynamics images. IEEE Trans. Inf. Forensics Secur. 2014, 9, 1780–1789. [Google Scholar] [CrossRef]
  93. Feng, T.; Yang, J.; Yan, Z.; Tapia, E.; Shi, W. TIPS: Context-aware implicit user identification using touch screen in uncontrolled environments. In Proceedings of the 15th Workshop on Mobile Computing Systems and Applications (HotMobile ’14), Santa Barbara, CA, USA, 26–27 February 2014. [Google Scholar] [CrossRef]
  94. Jain, A.; Kanhangad, V. Exploring orientation and accelerometer sensor data for personal authentication in smartphones using touchscreen gestures. Pattern Recognit. Lett. 2015, 68, 351–360. [Google Scholar] [CrossRef]
  95. Holz, C.; Knaust, M. Biometric touch sensing: Seamlessly augmenting each touch with continuous authentication. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST ’15), Charlotte, NC, USA, 11–15 November 2015; pp. 303–312. [Google Scholar] [CrossRef]
  96. Ahmad, J.; Sajjad, M.; Jan, Z.; Mehmood, I.; Rho, S.; Baik, S. Analysis of interaction trace maps for active authentication on smart devices. Multimed. Tools Appl. 2017, 76, 4069–4087. [Google Scholar] [CrossRef]
  97. Meng, W.; Wang, Y.; Wong, D.; Wen, S.; Xiang, Y. TouchWB: Touch behavioral user authentication based on web browsing on smartphones. J. Netw. Comput. Appl. 2018, 117, 1–9. [Google Scholar] [CrossRef]
  98. Liang, X.; Zou, F.; Li, L.; Yi, P. Mobile terminal identity authentication system based on behavioral characteristics. Int. J. Distrib. Sens. Netw. 2020, 16. [Google Scholar] [CrossRef]
  99. Frank, M.; Biedert, R.; Ma, E.; Martinovic, I.; Song, D. Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans. Inf. Forensics Secur. 2013, 8, 136–148. [Google Scholar] [CrossRef]
  100. De Luca, A.; Hang, A.; Brudy, F.; Lindner, C.; Hussmann, H. Touch me once and i know it’s you! Implicit authentication based on touch screen patterns. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’12), Austin, TX, USA, 5–10 May 2012; pp. 987–996. [Google Scholar] [CrossRef]
  101. Feng, T.; Liu, Z.; Kwon, K.A.; Shi, W.; Carbunar, B.; Jiang, Y.; Nguyen, N. Continuous mobile authentication using touchscreen gestures. In Proceedings of the 2012 IEEE Conference on Technologies for Homeland Security (HST), Waltham, MA, USA, 13–15 November 2012; pp. 451–456. [Google Scholar] [CrossRef]
  102. Brocardo, M.; Traore, I.; Woungang, I. Authorship verification of e-mail and tweet messages applied for continuous authentication. J. Comput. Syst. Sci. 2015, 81, 1429–1440. [Google Scholar] [CrossRef]
  103. Kaur, R.; Singh, S.; Kumar, H. TB-CoAuth: Text based continuous authentication for detecting compromised accounts in social networks. Appl. Soft Comput. J. 2020, 97. [Google Scholar] [CrossRef]
  104. Brocardo, M.; Traore, I.; Woungang, I. Toward a framework for continuous authentication using stylometry. In Proceedings of the 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, Victoria, BC, Canada, 13–16 May 2014; pp. 106–115. [Google Scholar] [CrossRef]
  105. Saevanee, H.; Clarke, N.; Furnell, S.; Biscione, V. Text-based active authentication for mobile devices. IFIP Adv. Inf. Commun. Technol. 2014, 428, 99–112. [Google Scholar] [CrossRef]
  106. Fridman, L.; Stolerman, A.; Acharya, S.; Brennan, P.; Juola, P.; Greenstadt, R.; Kam, M.; Gomez, F. Multi-modal decision fusion for continuous authentication. Comput. Electr. Eng. 2015, 41, 142–156. [Google Scholar] [CrossRef]
  107. Joyce, R.; Gupta, G. Identity Authentication Based on Keystroke Latencies. Commun. ACM 1990, 33, 168–176. [Google Scholar] [CrossRef]
  108. Gascon, H.; Uellenbeck, S.; Wolf, C.; Rieck, K. Continuous authentication on mobile devices by analysis of typing motion behavior. In Sicherheit 2014—Sicherheit, Schutz und Zuverlässigkeit; Katzenbeisser, S., Lotz, V., Weippl, E., Eds.; Gesellschaft für Informatik: Bonn, Germany, 2014. [Google Scholar]
  109. Giuffrida, C.; Majdanik, K.; Conti, M.; Bos, H. I sensed it was you: Authenticating mobile users with sensor-enhanced keystroke dynamics. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment; Springer: Berlin/Heidelberg, Germany, 2014; pp. 92–111. [Google Scholar]
  110. Cilia, D.; Inguanez, F. Multi-model authentication using keystroke dynamics for smartphones. In Proceedings of the 2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin), Berlin, Germany, 2–5 September 2018; pp. 1–6. [Google Scholar]
  111. Anusas-Amornkul, T. Strengthening password authentication using keystroke dynamics and smartphone sensors. In Proceedings of the 9th International Conference on Information Communication and Management, Prague, Czech Republic, 23–26 August 2019; pp. 70–74. [Google Scholar]
  112. Monrose, F.; Rubin, A.D. Keystroke dynamics as a biometric for authentication. Future Gener. Comput. Syst. 2000, 16, 351–359. [Google Scholar] [CrossRef]
  113. Chang, T.Y.; Tsai, C.J.; Lin, J.H. A graphical-based password keystroke dynamic authentication system for touch screen handheld mobile devices. J. Syst. Softw. 2012, 85, 1157–1165. [Google Scholar] [CrossRef]
  114. Yazji, S.; Chen, X.; Dick, R.P.; Scheuermann, P. Implicit user re-authentication for mobile devices. In International Conference on Ubiquitous Intelligence and Computing; Springer: Berlin/Heidelberg, Germany, 2009; pp. 325–339. [Google Scholar]
  115. Gomi, H.; Yamaguchi, S.; Tsubouchi, K.; Sasaya, N. Continuous authentication system using online activities. In Proceedings of the 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), New York, NY, USA, 1–3 August 2018; pp. 522–532. [Google Scholar]
  116. Mahbub, U.; Komulainen, J.; Ferreira, D.; Chellappa, R. Continuous authentication of smartphones based on application usage. IEEE Trans. Biom. Behav. Identity Sci. 2019, 1, 165–180. [Google Scholar] [CrossRef]
  117. Neal, T.J.; Woodard, D.L.; Striegel, A.D. Mobile device application, bluetooth, and wi-fi usage data as behavioral biometric traits. In Proceedings of the 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), Arlington, VA, USA, 8–11 September 2015; pp. 1–6. [Google Scholar]
  118. Ying, J.J.C.; Chang, Y.J.; Huang, C.M.; Tseng, V.S. Demographic prediction based on users mobile behaviors. Mob. Data Chall. 2012, 2012, 1–4. [Google Scholar]
  119. Solomon, A.; Bar, A.; Yanai, C.; Shapira, B.; Rokach, L. Predict demographic information using word2vec on spatial trajectories. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP ’18), Singapore, 8–11 July 2018; pp. 331–339. [Google Scholar]
  120. Crivellari, A.; Beinat, E. From motion activity to geo-embeddings: Generating and exploring vector representations of locations, traces and visitors through large-scale mobility data. ISPRS Int. J. Geo-Inf. 2019, 8, 134. [Google Scholar] [CrossRef] [Green Version]
Sensors 22 06980 g001 550
Figure 1. Research methodology.
Figure 1. Research methodology.
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Figure 2. WoX reference model. WoX simplifies the Application layer as it wraps the specific Presentation Objects (PO), Business Objects (BO) and Data Objects (DO) for each (OBServable) thing. By this way, it reduces the application volume.
Figure 2. WoX reference model. WoX simplifies the Application layer as it wraps the specific Presentation Objects (PO), Business Objects (BO) and Data Objects (DO) for each (OBServable) thing. By this way, it reduces the application volume.
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Figure 3. MOF model.
Figure 3. MOF model.
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Figure 4. Solution workflow.
Figure 4. Solution workflow.
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Figure 5. Example: Behavior graph after 1 day.
Figure 5. Example: Behavior graph after 1 day.
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Figure 6. Example: Behavior graph after 2 months.
Figure 6. Example: Behavior graph after 2 months.
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Figure 7. Example: Behavior graph after 4 months.
Figure 7. Example: Behavior graph after 4 months.
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Table
Table 1. Habits and expectations from the cohort.
Table 1. Habits and expectations from the cohort.
Habit IDHabit DescriptionExpectation from WoX+Continuous Authentication Expectation
1Every 2 months, when the decalcification warning light on the coffee machine turns on, I buy the decalcifier on AmazonI expect that if the decalcification warning light is on, the decalcifier should be automatically boughtI expect that if the decalcifier warning light is on and I have bought the decalcifier at the supermarket, I can also buy alcohol without showing my ID
2At the end of the month, when I receive my salary, I pay the energy billI expect that if I receive extra money in a different day of the month, pending bills are automatically paidI expect that if I am at Walmart and I’m paying the energy bill in any day of the month, I can book and pay my taxi without using the second factor authentication
3Every business day, at 7:40 a.m. , 8:10 a.m. or 1:20 p.m. I buy the bus ticket on the company’s websiteI expect that if I’m at the bus stop at 7:30 and I forgot to buy my ticket, the system should automatically buy it for meI expect that if my parents are accompanying me at the University campus, I could move the price of the ticket to the digital piggy bank without authorizing the transaction
4In the summer, during the weekend and when I come back from the beach, I buy the car’s perfume after washing the car at around 21:30I expect that, if I am at the washing car service after being at the beach, an automatic purchase should be triggeredI expect that if it is weekend and I am at the beach, the system should book and pay a washing service for me
5When I go shopping, if I like a dress but my size is finished, in the late evening when the baby sleeps I search and buy the same dress on the InternetI expect that if I am in a clothes shop and I scan the barcode of a dress, it should automatically put my size in the shopping cartI expect that if I’m at home in the late night and I’m buying a cloth, I can order my dinner without the second step verification
6When the car notifies me on the app that I reached the number of km for tires, I buy themI expect that I should receive a list of quotations for different tires when the car reaches the km thresholdI expect that if I’m paying the tires after the notification from the app, I can book and pay a parking lot for the next day using Amazon Alexa
7When I park the car near the bus stop, I buy the bus ticketI expect that if I park the car near the bus stop, the bus ticket is automatically boughtI expect that if the parking lot is the one of the municipality, I can pay my taxes at the totem without inserting my password
8In the summer, I buy 3 antiparos vials per monthI expect that if the average weather temperature is above a certain threshold, the antiparos are automatically boughtI expect that if I’m paying the antiparos at the pet shop and it is summer, then I can go to the bank branch beside the pet shop and interact with the ATM by only using my voice
9Every tenth day of the month I send a bank transfer to pay my rentI expect that, if there are enough money on my account, the rent will be paid automaticallyI expect that if I a.m. at paying my rent on the 10th, I can login to the banking app without logging in
10When the gasoline price is low, and around the beginning and the half of the month, I fill the tankI expect that if I’m driving, the fuel price is low and the tank is below a certain threshold, the navigator app will suggest me the most convenient gas stationI expect that if I’m filling the tank in the most convenient gas station, than I can pay oil check without inserting the PIN
Table
Table 2. Example of offset probability distributions starting from time_scale values.
Table 2. Example of offset probability distributions starting from time_scale values.
time _ scale Q 1 Q 3 IQR
1−0.67450.67451.349
50−33.72533.72567.45
100−67.4567.45134.9
500−337.25337.25674.5
Table
Table 3. Requirements satisfaction.
Table 3. Requirements satisfaction.
Req IDRequirementSatisfiedComment
1A sensed value should be independent from the specific device that generates the readingYesWoX satisfies this requirement
2Mined habits should be identifiable over different physical setupsYesWoX satisfies this requirement
3A habits-matching layer should be flexible enough to recognize with a certain precision a typical habits even if not all the exact conditions occurYesThe ML block satisfies this requirement
4Authorization-based services should be informed about the opening (or closing) of a secure session for a specific user, fired by the detection of a habitApplicableWoX satisfies this requirement, but it has not been tested yet
5Both temporal and spatial information should be provided to open a contextual secure session in time and spaceApplicableNot yet provided
6The user should tell authorization-based services who s/he claim to be, prior to use the service in frictionless modeApplicableNot yet implemented
7The way the user claims to be himself should be constant among the different auth-based scenariosApplicableNot yet implemented
8The way the user claims to be himself should be independent from the media used (i.e., mobile-based BLE or WiFi, smartcard)YesWoX satisfies this requirement
9A spatio-temporal matching engine should fuzzy-match different units of time and taxonomies of locations, both hierarchical and flatApplicableThere are some already studied algorithms [47] satisfying this requirement.
10Non personal sensing devices spread across the smart city should feed personal habits scenariosNoIt must be found a method for data incoming public IoT infrastructure to trigger user-specific rules
Table
Table 4. Comparison and discussion of our approach against existing studies.
Table 4. Comparison and discussion of our approach against existing studies.
CA typeTechniquesStudiesObtrusiveness Discussion
PhysiologicalFace[49,50,51,52,53,54]User should stay still in front of camera
Voice[55,56,57,58,59]User should talk, even if the use case does not foresee voice interaction
EEG[60,61,62]Electrodes must be placed on the user’s scalp
ECG[63,64,65]User must at least wear a wearable device (e.g., Apple Watch)
Eye movement[66,67,68,69]A still camera in front of user’s face is needed for eye tracking
Eye blink[70]As above
BioAura[71]Wearable medical devices should be continuously worn
Multimodal[72,73,74,75,76,77,78]A combination of the above methods is even more cumbersome
BehavioralMotion Dynamics[79,80,81,82,83,84,85,86,87,88]Gait-based authentication is not so invasive if only a smartphone is needed. Anyway, a smartphone is always needed in the user’s pocket.
Touch Dynamics[89,90,91,92,93,94,95,96,97,98,99,100,101]Limited to recognizing the user when a touch screen is involved (gestures, swipes, or tapping on the screen)
Stylometry Dynamics[102,103,104,105,106]Limited to use cases when writing is demanded to the user
Keystroke Dynamics[107,108,109,110,111,112,113]Limited to use cases where a keyboard (physical or virtual) is involved
Eye movement[66,67,68,69]Eye tracking equipment is needed
Eye blink[70]As above
BioAura[71]Wearable medical devices should be continuously worn
Context-basedFile system, Network Access, GPS, Online activity, app usage, Bluetooth, Wi-Fi[114,115,116,117,118,119,120]Very close to this paper idea, no encumbrance, but the current studies do not include the interaction with smart environments
WoX+User daily habits mined from smart environments like smart home and smart citiesThis workNo obtrusiveness because the system adapts with any personal data source incoming from the environments
Table
Table 5. Performance calculus for a single day of data.
Table 5. Performance calculus for a single day of data.
IDNumber of SensorsNumber of InteractionsMining Time (ms)
1551275
25102047
35202236
45504434
51051490
610101950
710202398
810504269
91551164
1015102247
1115202718
1215503258
132051402
1420102205
1520202944
1620503468
173051102
1830101608
1930202824
2030503816
215051331
2250101608
2350202266
2450503132
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Mainetti, L.; Panarese, P.; Vergallo, R. WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City. Sensors 2022, 22, 6980. https://doi.org/10.3390/s22186980

AMA Style

Mainetti L, Panarese P, Vergallo R. WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City. Sensors. 2022; 22(18):6980. https://doi.org/10.3390/s22186980

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Mainetti, Luca, Paolo Panarese, and Roberto Vergallo. 2022. "WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City" Sensors 22, no. 18: 6980. https://doi.org/10.3390/s22186980

APA Style

Mainetti, L., Panarese, P., & Vergallo, R. (2022). WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City. Sensors, 22(18), 6980. https://doi.org/10.3390/s22186980

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