Litcius/Paper detail

Weaknesses of Popular and Recent Covert Channel Detection Methods and a Remedy

Sebastian Zillien, Steffen Wendzel

2023IEEE Transactions on Dependable and Secure Computing20 citationsDOIOpen Access PDF

Abstract

Network covert channels are applied for the secret exfiltration of confidential data, the stealthy operation of malware, and legitimate purposes, such as censorship circumvention. In recent decades, some major detection methods for network covert channels have been developed. In this paper, we investigate two highly cited detection methods for covert timing channels, namely <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -similarity and compressibility score from Cabuk et al. (jointly cited by 930 papers and applied by thousands of researchers). We additionally analyze two recent ML-based detection methods: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAS</i> (2022) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SnapCatch</i> (2021). While all these detection methods must be considered valuable for the analysis of typical covert timing channels, we show that these methods are not reliable when a covert channel's behavior is slightly modified. In particular, we demonstrate that when confronted with a simple covert channel that we call <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> libur, all detection methods can be circumvented or their performance can be significantly reduced although the covert channel still provides a high bitrate. In comparison to previous timing channels that circumvent these methods, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> libur is much simpler and eliminates the need of altering previously recorded traffic. Moreover, we propose an enhanced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -similarity that can detect the classical covert timing channel as well as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> libur.

Topics & Concepts

Covert channelCovertComputer scienceChannel (broadcasting)NotationAlgorithmInformation retrievalMathematicsArithmeticComputer networkPhilosophyOperating systemCloud computingCloud computing securitySecurity information and event managementLinguisticsInternet Traffic Analysis and Secure E-votingAdversarial Robustness in Machine LearningWireless Signal Modulation Classification