Covert Channel Detection: Machine Learning Approaches

• Published in: IEEE access Vol. 10; pp. 38391 - 38405
• Main Authors: Elsadig, Muawia A., Gafar, Ahmed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Channels; Classification algorithms; Computer networks; cover timing channel; covert channel detection; covert storage channel; covert traffic; deep learning; Internet of Things; Machine learning; network covert channels; network traffic; overt traffic; Protocols; Receivers; Robustness; Security; Switches; Timing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3164392

The advanced development of computer networks and communication technologies has made covert communications easier to construct, faster, undetectable and more secure than ever. A covert channel is a path through which secret messages can be leaked by violating a system security policy. The detection of such dangerous, unwatchable, and hidden threats is still one of the most challenging aspects. This threat exploits methods that are not dedicated to communication purposes, meaning that traditional security measures fail to detect its existence. This review has introduced a brief introduction of covert channel definitions, types and developments, with a particular focus on detection techniques using machine learning (ML) approaches. It provides a thorough review of the most common covert channels and ML techniques that are used to counter them, as well as addressing their achievements and limitations. In addition, this paper introduces a comparative experimental study for some common ML approaches that are commonly used in this field. Accordingly, the performance of these classifiers was evaluated and reported. The paper concludes that our information is still at risk, nothing is said to be secured and more work on the detection of covert channels is required.