Covert channel cyber-attack over video stream DCT payload: (Copyright protection algorithm for video and audio streams)

Yoram Segal, Ofer Hadar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The two main cyber-attack techniques via video packets are based on using the packet header or the payload. Most of the standard software protection tools easily detect anomalies in headers since there are fewer places to embed the malicious content. Moreover, due to the relatively small header size, such attacks are limited by the data volumes that can transfer. On the other hand, a cyber-attack that uses video packets’ payload can effectively conceal much more information and produce covert channels. Multimedia covert channels provide reasonable bandwidth and long-lasting transmission streams, suitable for planting malicious information and therefore used as an exploit alternative. The primary focus of this article is a proof of concept of cyber-attack that conceals malicious data in a video payload in the compressed domain, using steganography (in real time). This malicious data is extracted using a covert channel and a malware (that had previously planted at the end user side), on the other side. Additionally, after the implementation of the attack, it is necessary to review its parameters and conclude what the optimal parameters to use in different video scenarios. In this paper, we will demonstrate attacks that take advantage of compressed domain video payload. It is important to note that this method can be used as a method of copyright protection.

Original languageEnglish
Title of host publicationCyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings
EditorsShlomi Dolev, Danny Hendler, Sachin Lodha, Moti Yung
PublisherSpringer Verlag
Pages47-66
Number of pages20
ISBN (Print)9783030209506
DOIs
StatePublished - 1 Jan 2019
Event3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019 - Beer Sheva, Israel
Duration: 27 Jun 201928 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11527 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019
Country/TerritoryIsrael
CityBeer Sheva
Period27/06/1928/06/19

Keywords

  • Cyber
  • DCT
  • Discrete Cosine Transform
  • Exploit
  • Inter prediction
  • Intra prediction
  • Invisible covert channel
  • Motion vectors
  • Steganography
  • Steno objects
  • Watermarking

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