Name
Title | ||
|---|---|---|
Deep Learning Based Approach for Classifying Power Signals and Detecting Anomalous Behavior of Wireless Devices |
Abstract | ||
|---|---|---|
The problem of extracting insights from signals is a very interesting and challenging task. This problem finds its way into the task of detecting malware in wireless devices by considering their power consumption signals. Relying on the fact that every single action on-board (whether hardware or software driven actions) will be reflected as a change in the device's power consumption; consequently, leaving a trace (by malware) in the power consumed by the device is something inevitable. Motivated by the powerful capabilities of deep learning in extracting features unsupervisedly, this paper proposes deep learning based detection methodology. The methodology makes use of time-frequency representation (TFR) of signals to resemble informative visual textures. The assumption is that TFRs (2-D images) construct textures that capture valuable information out of 1-D signals. Following that, Histograms of Oriented Gradients (HOG) of TFR images are computed. The HOG information is treated as images that contain better discriminative features. Finally, a convolutional neural network (CNN) model is trained to accurately classify these signals and detect the anomalous behavior. We have validated the effectiveness of the proposed methodology on a cybersecurity application in the domain of wireless devices. The experimental results confirm that proposed methods can be used to detect the presence of malwares in smartphones with high accuracy, and can also outperform previously reported methods with ~9% to 17% detection performance gain. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SERVICES.2019.00030 | 2019 IEEE World Congress on Services (SERVICES) |
Keywords | Field | DocType |
Deep Learning, CNN, Signals Classification, Malware Detection, Histogram of Oriented Gradients, HOG | Data mining,Histogram,Wireless,Pattern recognition,Convolutional neural network,Computer science,Histogram of oriented gradients,Software,Artificial intelligence,Deep learning,Malware,Discriminative model | Conference |
Volume | ISSN | ISBN |
2642-939X | 2378-3818 | 978-1-7281-3852-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdurhman Albasir | 1 | 44 | 4.66 |
| Ricardo Manzano | 2 | 0 | 0.68 |
| Kshirasagar Naik | 3 | 846 | 73.83 |