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Covert Timing Channels Detection Based on Auxiliary Classifier Generative Adversarial Network
- Source :
- IEEE Open Journal of the Computer Society, Vol 2, Pp 407-418 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Covert timing channels (CTCs) are defined as a mechanism that embeds covert information into network traffic. In a manner, information leakage caused by CTCs brings serious threat to network security. In recent years, detection of CTCs is a focus and a challenging task in the field of covert channel research. However, existing detection schemes based on statistical methods have poor performance in detecting multiple CTCs, and require so many inter-arrival times of packets that these schemes cannot detect CTCs in real time. In this paper, we propose a novel deep learning approach for CTCs detection, namely, covert timing channels detection based on auxiliary classifier generative adversarial network (CD-ACGAN). The network structure and loss function of CD-ACGAN are designed to be suitable for CTCs detection task. We first encode traffic flows into single-channel Gramian Angular Field (GAF) images. Then we use CD-ACGAN to learn features from GAF images and predict the classes of CTCs. Our experimental results show that our approach has high accuracy and strong robustness in detecting various CTCs.
- Subjects :
- Network security
business.industry
Network packet
Computer science
Deep learning
Gramian angular fields
generative adversarial network
Covert channel
deep learning
Pattern recognition
QA75.5-76.95
Information technology
T58.5-58.64
Robustness (computer science)
Covert
Covert timing channel
Electronic computers. Computer science
Classifier (linguistics)
Information leakage
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 26441268
- Volume :
- 2
- Database :
- OpenAIRE
- Journal :
- IEEE Open Journal of the Computer Society
- Accession number :
- edsair.doi.dedup.....2907dd48a458e68e340d4a952640ab9d