Abstract
Aim: A major problem in the Internet of Things (IoT) and Cyber-Physical System (CPS) devices is the detection of security threats in an efficient manner. Several recent incidents confirm that despite of the existing security solutions, security threats (e.g., malware and availability attacks) can still find their ways to such devices causing severe damages.
Methods: In this paper, we propose a methodology that leverages the power consumption of wireless devices and Restricted Boltzmann Machine (RBM) Autoencoders (AE) to build a model that makes them more robust to the presence of security threats. The method consists of two stages: (i) Feature Extraction where stacked RBM 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 (OC-SVM) is trained to perform the detection task.
Results: The validation of the methodology is performed on real measurement datasets and covers a wide range of security threats (namely, malware, DDOS, and cryptojacking). The obtained results show good potential throughout the five datasets 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 most of scenarios.
Conclusion: The performance of the proposed methodology shows a good generalization for detecting different security threats, and, hence, confirms the usefulness and applicability of the proposed approach.
Methods: In this paper, we propose a methodology that leverages the power consumption of wireless devices and Restricted Boltzmann Machine (RBM) Autoencoders (AE) to build a model that makes them more robust to the presence of security threats. The method consists of two stages: (i) Feature Extraction where stacked RBM 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 (OC-SVM) is trained to perform the detection task.
Results: The validation of the methodology is performed on real measurement datasets and covers a wide range of security threats (namely, malware, DDOS, and cryptojacking). The obtained results show good potential throughout the five datasets 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 most of scenarios.
Conclusion: The performance of the proposed methodology shows a good generalization for detecting different security threats, and, hence, confirms the usefulness and applicability of the proposed approach.
| Original language | English |
|---|---|
| Pages (from-to) | 1-25 |
| Number of pages | 25 |
| Journal | Journal of Surveillance, Security and Safety |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 15 Jan 2021 |
Bibliographical note
© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Keywords
- Malware Detection
- RBM Autoencoder
- IoT Devices
- Deep Learning
- Power Consumption Information