Using Deep Learning to Classify Power Consumption Signals of Wireless Devices: An Application to Cybersecurity

The problem of detecting malware in mobile devices is becoming increasingly important. While most of the mobile devices run on very limited resources, having anti-viruses installed on-board is not very practical, especially in IoT devices. Even if such tools exist, malware could hide or manipulate t...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2032 - 2036
Main Authors Albasir, A., James, R. Soundar Raja, Naik, K., Nayak, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
Deep Learning
Machine learning
Malware
Malware Detection
Monitoring
Neurons
Power demand
Signals Classification
Smart phones
Training
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Summary:The problem of detecting malware in mobile devices is becoming increasingly important. While most of the mobile devices run on very limited resources, having anti-viruses installed on-board is not very practical, especially in IoT devices. Even if such tools exist, malware could hide or manipulate their fingerprint, making them not easy to detect. Thus, having effective countermeasures for after malware intrusion is paramount. In this work, we utilize deep learning ability to learn multiple levels of representations from raw data to classify power consumption signals obtained from smartphones. The objective is to build a framework that can intelligently tell if the smartphone has a malware or not by only monitoring its power consumption. Validation tests confirm that the proposed framework show that information contained in the measured power consumption of smartphones can in principle be used to identify malware existence and further can tell how active malware is with very high accuracy.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8461304