Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Concurrency and Comp...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Concurrency and Computation Practice and Experience
Article . 2024 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
DBLP
Article . 2025
Data sources: DBLP
versions View all 2 versions
addClaim

A Review on Network Covert Channel Construction and Attack Detection

Authors: Mrinal Ashish Khadse; Dhananjay Manohar Dakhane;

A Review on Network Covert Channel Construction and Attack Detection

Abstract

ABSTRACTA covert network channel is a communication channel in which the message is secretly transmitted to the recipient. Sometimes, covert network channels are vulnerable to multiple attacks. Therefore, the message must be properly secure. In most cases, the covert channel is used to ensure data protection and allow users to freely access the Internet. In this paper, several recent studies are reviewed on covert network channels and examine the existing works from 2015 to 2024. This review article also discusses the undetectability and reliability of different types of covert network channels. Furthermore, a detailed description of the covert network channel's ability to hide in containers is provided. Existing research on covert network channels explains a few techniques for detecting attacks in secret data communication. However, several machine learning and deep learning techniques have been discussed in this article. Additionally, this article describes the accuracy of detection through an overview of current technologies. In addition, various countermeasures to prevent attacks in covert channels are also discussed in detail. However, in this case, the bandwidth limitations, data set limitations, and covert channel capacity are clearly defined, which will help future researchers build covert network channels and detect attacks. Finally, this work considers the challenges faced by covert network channels and the future scope of application.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Top 10%
Top 10%
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!