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research-article

A Power Signal Based Dynamic Approach to Detecting Anomalous Behavior in Wireless Devices

Authors:

Robin Joe Prabhahar Soundar Raja James,

Abdurhman Ali Albasir,

Kshirasagar Naik,

Nishith GoelAuthors Info & Claims

MobiWac'18: Proceedings of the 16th ACM International Symposium on Mobility Management and Wireless Access

Pages 9 - 18

https://doi.org/10.1145/3265863.3265867

Published: 25 October 2018 Publication History

Abstract

The health and security of wireless devices are fast gaining importance, and these are vital for effective implementation of sensor networks and Internet of Things (IoT). Any device, wired or wireless, needs a power source, and the power consumed is a consequence of its usage and functionality. In this context, this paper proposes a methodology to detect anomalous behavior of wireless devices by monitoring their power consumption patterns. The proposed methodology utilizes Independent Component Analysis (ICA) to extract information from the current power consumption of the device and generates features of the state of the device by calculating the degree of similarity of the extracted information with the known normal behavior of the device. Then, Recursive Feature Elimination (RFE) is used to select features from the generated feature vector. Finally, Classification algorithms are used to classify and detect the anomalous behavior. We have validated the methodology by emulating anomalous behavior on smartphones through a custom designed app that runs in the background while the main app is being used. Validation results indicate that the proposed methodology can be used to identify even a sparsely active malware existence with very high accuracy. The proposed model has an accuracy of 88% for a malware active for 1% of the total time and accuracy of almost 100% for malware active for 12% of the time.

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Cited By

Albasir ANaik KManzano R(2023)Toward Improving the Security of IoT and CPS Devices: An AI ApproachDigital Threats: Research and Practice10.1145/34978624:2(1-30)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3497862
Siddiqui ABoukerche A(2020)TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of ThingsCluster Computing10.1007/s10586-020-03153-8Online publication date: 2-Sep-2020
https://doi.org/10.1007/s10586-020-03153-8
Al-tekreeti MKapoor TManzano RAlbasir ANaik KGoel NKozlowski A(2019)Machine Learning Based Malware Detection in Wireless Devices Using Power Footprints2019 International Symposium on Systems Engineering (ISSE)10.1109/ISSE46696.2019.8984518(1-8)Online publication date: Oct-2019
https://doi.org/10.1109/ISSE46696.2019.8984518
Show More Cited By

Index Terms

A Power Signal Based Dynamic Approach to Detecting Anomalous Behavior in Wireless Devices
1. Networks
  1. Network properties
    1. Network security
      1. Mobile and wireless security
2. Security and privacy

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Published In

cover image ACM Conferences

MobiWac'18: Proceedings of the 16th ACM International Symposium on Mobility Management and Wireless Access

October 2018

140 pages

ISBN:9781450359627

DOI:10.1145/3265863

General Chair:
Wessam Ajib
University of Quebec de Montreal, Canada
,
Program Chairs:
Rodolfo W. L. Coutinho
University of Ottawa, Canada
,
Frank Li
University of Agder, Norway

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2018

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Natural Sciences and Engineering Research Council of Canada

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Overall Acceptance Rate 83 of 272 submissions, 31%

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Cited By

Albasir ANaik KManzano R(2023)Toward Improving the Security of IoT and CPS Devices: An AI ApproachDigital Threats: Research and Practice10.1145/34978624:2(1-30)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3497862
Siddiqui ABoukerche A(2020)TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of ThingsCluster Computing10.1007/s10586-020-03153-8Online publication date: 2-Sep-2020
https://doi.org/10.1007/s10586-020-03153-8
Al-tekreeti MKapoor TManzano RAlbasir ANaik KGoel NKozlowski A(2019)Machine Learning Based Malware Detection in Wireless Devices Using Power Footprints2019 International Symposium on Systems Engineering (ISSE)10.1109/ISSE46696.2019.8984518(1-8)Online publication date: Oct-2019
https://doi.org/10.1109/ISSE46696.2019.8984518
Manzano RAlbasir ANaik KKozlowski JGoel N(2019)Detection of Anomalous Behavior in Wireless Devices Using Changepoint Analysis2019 IEEE International Congress on Internet of Things (ICIOT)10.1109/ICIOT.2019.00026(82-90)Online publication date: Jul-2019
https://doi.org/10.1109/ICIOT.2019.00026

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