Sayed Abdulhayan,
Mohammed Nazeem,
Mohammed Rakheeb Chowdary,
Mohammed Rashid,
Mukthar Ahmed Ali,
- Professor, P A College of Engineering, Mangalore, India
- Student, P A College of Engineering, Mangalore, India
- Student, P A College of Engineering, Mangalore, India
- Student, P A College of Engineering, Mangalore, India
- Student, P A College of Engineering, Maharashtra, India
Abstract
With the rapid increase in data exfiltration due to cyber-attacks, Covert Timing Channels (CTCs) have emerged as a significant and sophisticated network security threat. These channels exploit inter-arrival times of data packets to exfiltrate sensitive information from targeted networks. Detecting CTCs increasingly relies on machine learning techniques, which use statistical metrics to differentiate between malicious (covert) and legitimate (overt) traffic flows. However, as cyber-attacks become more adept at evading detection and the prevalence of CTCs grows, there is a critical need to enhance both the performance and precision of detection methods. This is essential to effectively identify and prevent CTCs while mitigating the reduction in quality of service that can result from the detection process. In this paper, we introduce an innovative image-based solution for fully automated detection and localization of CTCs. Our approach leverages the insight that covert channels generate traffic patterns that can be visualized as colored images. Using this concept, our solution is designed to automatically detect and pinpoint the malicious segments (i.e., specific packets) within a traffic flow. By isolating the covert portions of traffic, our method minimizes the negative impact on the quality of service that would occur if entire traffic flows were blocked due to detected covert channels.
Keywords: Covert Timing Channels, Elliptic Curve Cryptography (ECC), Reserved Sockets
[This article belongs to Journal of Control & Instrumentation(joci)]
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Journal of Control & Instrumentation
| Volume | 15 |
| Issue | 02 |
| Received | June 18, 2024 |
| Accepted | July 6, 2024 |
| Published | August 8, 2024 |