Machine learning and deep learning approaches in IoT

Review plan

Figures 1 and 2 show the methodology that defines the research process for the classification scheme, relevant publications, and publications mapping criteria. A search strategy has been implemented to find all the related data of IoT. A complete systematic approach is used to select the relevant studies without biases. In this review, the structured process has been followed that involved:

• Research objectives

• Research questions

• Organizing search repositories

• Selection studies

• Screening results

• Data extraction

• Results

• Review report finalization

To achieve the objectives of the above-defined review plan, see the RQs in Table 2:

• The RQ1 identifies and explores different high-level databases that have been published in the literature on IoT-related smart devices security by using machine learning and deep learning. These answers might help choose the best venues from the highest priority platforms.

• RQ2 helps assist the primary study conducted within the last five years, which discusses the implementation of secure systems in IoT.

• RQ3 deals with the basic methods to implement authorization, authentication, and privacy in IoMT, IoV, and IPS environments.

• The objective of RQ4 is to implement machine learning and deep learning techniques to cover all the security issues faced by the IoMT.

Review conduct

In this systematic literature review, we have extracted the most relevant data from the selected digital databases. Furthermore, the predefined inclusion/exclusion criteria select the papers from the repositories. Moreover, the quality assessment is added to enhance the paper selection approach. After that, the most important papers are extracted from the existing literature by implementing snowballing techniques.

Automated search in digital repositories

Systematic search is implemented to extract the related data from the available online repositories and filter the irrelevant information. Moreover, manual and automatic search techniques have been applied while exploring the search terms. Different digital libraries were visited during this process, and only those repositories have been selected that are searched from our search process and commonly accessed literature survey. Those public venues are selected that are related to SLR. Google Scholar also added the venue that even accessed the data from the indirect venues. Therefore, the following digital venues are covered almost all the relevant searches selected as a primary source for automatic search:

• Google Scholar (https://scholar.google.com/)

• HEC Digital Library (http://www.digitallibrary.edu.pk/)

• ACM Digital Library (http://dl.acm.org)

• IEEE eXplore (http://ieeexplore.ieee.org)

• ScienceDirect (https://www.sciencedirect.com)

Manual search is implemented to collect more related literature on IoT machine learning techniques and their related domain. The extracted information will provide a limited search of the related data, so it is specified according to the given conditions:

• Primary keywords are selected based on the research questions

• Identify the secondary keywords that were used as additional keywords

• The search string is developed by adding the “AND” and “OR” Boolean operators

Primary keywords are chosen as key identifiers to search the IoT data. Secondary or additional keywords are added with the primary keyword to search the related data. Boolean operators, keywords, and wildcards have been added to develop the final search query.

Figure 3 defines the search query that is restrictive to appear during the initial search process. The query is unable to search the final string data. The final string query is too restrictive and searches only the related articles on Google scholar and other defined repositories. Moreover, after implementing the search query, related articles are selected that fulfill the defined criteria of the papers. Additionally, selecting studies with string queries is very effective compared to traditional systems. Figure 4 depicts the visualization of all possible combinations of the query string.

Review report

The final selected papers are inspected thoroughly and selected; the 50 papers are based on the search query and fulfill inclusion/exclusion criteria. Overview of the selected papers from the above query is mentioned in Table 4. Papers are excluded less than the four pages and filter papers according to the following parameters: since 2016, title, introduction, abstract, and conclusion. Finally, the papers are extracted with full articles. The paper count of per year is defined in the Fig. 5.

Figure 6 shows that most of the journal papers are added in this review paper, and the reports are skipped as they are not fulfilling the inclusion/exclusion criteria.

Figure 7 shows that the selected papers are from a different geographical areas, and most of them belong to the different states of America.

Which are relevant publication channels for IoT research?

IoT security is still a challenging domain in research due to the growth of IoT devices which causes security threats. There is a need to identify the proper publication tools and venues to access the relevant data to solve these security issues in the IoT domain. Moreover, this section presents knowledge base publication sources, types, and publication channels.

After the inspection phase, a maximum of eight publications are selected from the IEEE eXplore and one from the ACM journal, as mentioned in Table 5. These publications are considered the world’s largest professional publishing source. Table 6 presents all the publication channels from where the papers are selected for the current SLR. Table 7 discussed the contribution and proposed solutions that are provided by different authors related to current studies.

Moreover, Table 8 presents the quality score of the each study which determines the classification of the studies for the systematic literature review. These studies are classified based on empirical search, research type and methodology. Quality assessment score are used to categorized the studies that are included in the paper. These empirical studies are further classified such as surveys, evaluation studies, primary search and experimental search. On the basis of these classifications, research taxonomy is defined in later sections. The codes are assigned including IoV and IoM for Internet of Vehicle and Internet of Medical Things respectively. Future research is clearly defined the path for the new researcher to explore more relevant studies.

What are the current challenges in different IoT types regarding implementing security measures?

IoMT and IoV are the major areas in the IoT domain discussed regarding security, as these are sensitive to human life. In recent years, security measures have been implemented in these areas, such as authentication, authorization, and privacy (Tahsien, Karimipour & Spachos, 2020). Some current challenges discussed in Table 9 are faced with implementing the security measures. Moreover, the existing literature has not considered the authentication, authorization, and privacy of data in the IoMT and IoV domain using machine learning and deep learning techniques. Machine learning and deep learning algorithms are implemented to overcome these challenges to cover security issues (Cui et al., 2018; Stiawan, Abdullah & Idris, 2010).

What are some of the authorization and authentication methods used for general IoT security purposes?

According to the existing literature, Tama & Rhee (2017), security is the major issue in the IoT domain. Security methods are implemented to secure the IoT in all perspectives, including authentication and authorization (Uprety, Rawat & Li, 2021). A two-way authentication method provides the required security and resists attacks (Mahmood, 2020). If one factor is compromised, the second factor still provides enough security to the IoT system. Elliptic-curve cryptography (ECC) keys are mostly used for one-factor authentication (Bhatia, Verma & Sharma, 2020) as it provides overall lightweight and reliable protection. Moreover, biometric sensors are used as second-factor authentication for everyday use due to their convenient approach (Sikarwar & Das, 2021). Different methods of authentication and authorization are described in Table 10.

How can we implement or utilize lightweight ML-based security methods on resource-constrained IoMT devices?

Attackers mostly target the integrity and availability of the IoMT systems. AI techniques build detection models to avoid these attacks (Holbrook & Alamaniotis, 2019). Machine learning and deep learning models are used for intrusion detection. When any suspicious activities are detected in the system, then termination of the compromised connection is imposed to diminish the attack. In Table 11, ML-based lightweight methods are described in this study.

Taxonomic hierarchy

This systematic literature review aimed to implement the security parameters by selecting the relevant papers and critically reviewing them. We designed the taxonomic hierarchy of selected studies shown in Fig. 8 that are only focused on the security issues of the IoV domain. We have investigated the challenges and developments in different aspects, including high-level features, methods, and security areas. However, these aspects are further divided into sub-domains that show each aspect’s depth and their role in terms of secure devices. Table 12 shows the criteria, evaluation and findings of the papers that are added in the review.

General observations and future directions

This systematic literature review studies different machine learning and deep learning methods. We have reviewed an extensive number of studies; another IoT-related security is implemented in the domain of IoMT, IoV, and IPS. Moreover, selected studies were shortlisted considering the defined inclusion/exclusion criteria and quality assessment scoring. In addition, thematic analysis was performed to extract relevancy relations from these selected studies, which are coded.

The codes are selected from the existing literature shows in Table 13, as “IoMT” for the internet of medical things, “IoV” for the internet of the vehicle, and the “IPS” for intrusion prevention systems. After that, papers are selected that worked on machine learning and assigned them the code in the domain of machine learning which is “MM”, “MV”, and “MP”. In the last, papers are categorized according to the deep learning techniques that are “DM”, “DV”, and “DP”. Selected studies were carried out by assessing and analyzing their aims, methodologies, area of discussion, and limitations.

Furthermore, Table 14 defined the codes that are implemented on the selected papers from the defined query strings. All the IoT devices have low computing power, so there is a need to implement a security model covering these embedded devices’ authentication, authorizations, and privacy issues. A lightweight method is found to solve the security issues in the IoMT domain, as mentioned in the RQ4.

IoT domain faces huge challenges in IoMT, IoV, and IPS to security. Uprety, Rawat & Li (2021) investigated the challenges, including weak password protection, insecure interfaces, less data protection, and poor management of IoT devices. The main challenge identified in this article is authorization and authentication problems. Two-way factor analysis techniques are implemented to implement the authorization in IoT. Two-way factor authentications (Mahmood, 2020) are selected as the best security option and avoid attacks. If any security is compromised, then the other one provides essential security. Elliptic-curve cryptography (ECC) keys are used for first-factor authentication in IoMT and other domains of IoT, as they are lightweight and provide reliable protection. Security issues are also raised when the data is delivered over the internet, so constrained application protocols are used to overcome these security issues. Constrained application protocol (Kumar et al., 2019) is like the application protocol used for resource-constrained IoT applications, including IoMT and IoV.

Moreover, some attacks target the availability and integrity of the system, such as stepping-stone attacks. Therefore, the deep neural network is implemented to build the intrusion prevention system. However, important gaps need to be addressed to ensure the IoT devices are secure and not affected by any attacks on critical infrastructures.

The main objective of this SLR is to cover the security issues in the different domains of the internet of things by considering related articles. In order to accomplish the security issues, the hierarchical taxonomy of the selected articles is formulated in Fig. 8. At the top of the hierarchy discussed the internet of things as it is the major concern area. This taxonomy hierarchy shows the broader view of the SLR. It has inspected the different methods and security issues in IoT, including IoMT, IoV, and IPS. Furthermore, machine learning and deep learning methods are defined with the security issues of authorization, authentication, and privacy in the sub-levels to better understand the IoT and its domains.

Questions for primary study

According to the defined systematic literature review, we carried out the following shortcomings in the existing research.

• What are the other major security issues in IoT subdomains, and which intrusion detection and prevention system exists that covers all the security issues in IoT subdomains?

• Which model can be implemented for the security of IoT devices in all the domains, including IoMT, IoV, IoH, and IoT. Future research requires authentication, vulnerability, condentiality, authorization, and privacy methods.

• Most techniques used for IPS are not provided complete security on complex attacks. Future researchers can develop the intrusion prevention systems for IoT that can be implemented for multiple IoT subdomains to secure devices from all attacks.

• Different security methods are implemented according to the nature of the IoT domain, including a signature group scheme with various limitations. Therefore, Researchers are suggested to implement the security in IoT domains that protect the devices from different attacks to access better results.

• In the current era, heavy models are implemented in IoT devices to deal with complex and dynamic attacks. All the IoT devices have less computing power and cannot tackle this heavy software to overcome the security issues. Therefore, a lightweight method has been required that covers the security issues in the domain of IoT and provides the authentic model to secure these devices from vulnerable attacks.

As the enhancement in IoT, there is a need to implement more security issues to provide a secure IoT environment. Different hardware and software security parameters are implemented to protect the data from interruption and unauthorized access (Tahsien, Karimipour & Spachos, 2020; Mawgoud, Karadawy & Tawfik, 2019; Bhatia, Verma & Sharma, 2020; Vajar et al., 2021). The authentication, authorization, and privacy issues are currently discussed in IoT domains. However, some other security issues can also be addressed, such as secure data availability at the right time, resource authentication, integrity, and confidentiality (Das & Nene, 2017). The current state-of-the-art security models are not cover security in all the domains of IoT. Few of them are cover the IoMT security issues (Aljumaie et al., 2021; Raj & Madiajagan, 2021), and the remaining are focused on the IoV (Ali, Hassan & Saeed, 2021). Other domains exist in IoT, including IoH and IoT, which should also be secured from threats. In the current era, heavy models are implemented in IoT devices to deal with complex and dynamic attacks. All the IoT devices have less computing power and cannot tackle this heavy software to overcome the security issues. Different machine learning (Anbalagan et al., 2021) and deep learning techniques are implemented to maintain privacy in IoT. As the traditional deep learning models work with large data sets and for the training of that data, enormous computational power is required (Lv et al., 2020; Li et al., 2022). Therefore, a lightweight method has been required that covers the security issues in the domain of IoT and provides the authentic model to secure these devices from vulnerable attacks.