Unsupervised Detection of Anomalous Behavior in Wireless Devices based on Auto-Encoders

A. Albasir, Q. Hu, M. Al-Tekreeti, K. Naik, N. Naik, A. J. Kozlowski, N. Goel

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

A major problem of wireless devices is the detection of security threats in an efficient manner. Several recent incidents show that malicious applications (apps) can find their ways to online markets (e.g., Google Play Store) and be available for download and installation. Such malicious apps can collect sensitive data from millions of users and send them to a third-party servers. In this paper, we propose a methodology that leverages the power consumption of wireless devices to build a model that makes them more robust to the presence of malicious apps. The method consists of two stages: (i) Feature Extraction where stacked Restricted Boltzmann Machine (RBM) AutoEncoders (AE) and Principal Component Analysis (PCA) are used to extract features vector based on AE's reconstruction errors. (ii) Classifier where One-Class Support Vector Machine is trained to perform the classification task. The validation of the methodology is performed on a real measurements dataset. The obtained results show a good potential and prove that AEs' reconstruction error can be used as a good discriminating feature. The obtained detection accuracy surpasses previously reported techniques, where it reaches up to ~ 98% in some scenarios.

Original languageEnglish
Title of host publicationProceedings of IEEE/IFIP Network Operations and Management Symposium 2020
Subtitle of host publicationManagement in the Age of Softwarization and Artificial Intelligence, NOMS 2020
PublisherIEEE
ISBN (Electronic)9781728149738
DOIs
Publication statusPublished - 8 Jun 2020
Event2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020 - Budapest, Hungary
Duration: 20 Apr 202024 Apr 2020

Publication series

NameProceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020
PublisherIEEE
ISSN (Print)1542-1201
ISSN (Electronic)2374-9709

Conference

Conference2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
CountryHungary
CityBudapest
Period20/04/2024/04/20

Keywords

  • Denoising AutoEncoder
  • Malware Detection
  • Power Consumption Information
  • Wireless Devices

Fingerprint Dive into the research topics of 'Unsupervised Detection of Anomalous Behavior in Wireless Devices based on Auto-Encoders'. Together they form a unique fingerprint.

Cite this