CCgen: Injecting Covert Channels into Network Traffic

Iglesias, Félix; Meghdouri, Fares; Annessi, Robert; Zseby, Tanja

doi:10.1155/2022/2254959

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…This branch can optionally detail information about the experimental setup used to evaluate countermeasures. For instance, test data or test traffic might have been generated with particular tools, such as CCgen [185], BroCCaDe [186]- [188], WoDi-CoF [189] or CCHEF [190], or under specific conditions of an operating environment.…”

Section: A Using a Unified Description Methodsmentioning

confidence: 99%

A Generic Taxonomy for Steganography Methods

Wendzel¹,

Caviglione²,

Mazurczyk³

et al. 2022

Preprint

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A unified understanding of terms and their applicability is essential for every scientific discipline: steganography is no exception. Being divided into several domains (for instance, text steganography, digital media steganography, and network steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to some specific applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction of a pattern-based taxonomy for network steganography. Six years later, in 2021, the first work towards a pattern-based taxonomy for steganography was proposed. However, this initial attempt still faced several shortcomings, e.g., the lack of patterns for several steganography domains (the work mainly focused on network steganography and covert channels), various terminology issues, and the need of providing a tutorial on how the taxonomy can be used during engineering and scientific tasks, including the paper-writing process. As the consortium who published this initial 2021-study on steganography patterns, in this paper we present the first comprehensive pattern-based taxonomy tailored to fit all known domains of steganography, including smaller and emerging areas, such as filesystem steganography and cyber-physical systems steganography. Besides, to make our contribution more effective and promote the use of the taxonomy to advance research on steganography, we also provide a thorough tutorial on its utilization. Our pattern collection is available at <a href="https://patterns.ztt.hs-worms.de/" target="_blank">https://patterns.ztt.hs-worms.de</a>.

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Section: A Using a Unified Description Methodsmentioning

confidence: 99%

A Generic Taxonomy for Steganography Methods

Wendzel¹,

Caviglione²,

Mazurczyk³

et al. 2022

Preprint

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“…A covert channel is a channel that is exploited to transfer secret data covertly in a way violates system security policy [1], [2] a sender uses covert channels to communicate with a receiver in order to send him\her a covert message that cannot be seen by others [3]. In 1973, the concept of covert channel was presented by Lampson [4] to be executed in stand-alone machine environment.…”

Section: Introductionmentioning

confidence: 99%

An ensemble model to detect packet length covert channels

Elsadig

Gafar

2023

IJECE

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Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors.

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