Attack graph obfuscation

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

4 Scopus citations


Before executing an attack, adversaries usually explore the victim’s network in an attempt to infer the network topology and identify vulnerabilities in the victim’s servers and personal computers. In this research, we examine the effects of adding fake vulnerabilities to a real enterprise network to verify the hypothesis that the addition of such vulnerabilities will serve to divert the attacker and cause the adversary to perform additional activities while attempting to achieve its objectives. We use the attack graph to model the problem of an attacker making its way towards the target in a given network. Our results show that adding fake vulnerabilities forces the adversary to invest a significant amount of effort, in terms of time, exploitability cost, and the number of attack footprints within the network during the attack.

Original languageEnglish
Title of host publicationCyber Security Cryptography and Machine Learning - 1st International Conference, CSCML 2017, Proceedings
EditorsShlomi Dolev, Sachin Lodha
PublisherSpringer Verlag
Number of pages19
ISBN (Print)9783319600796
StatePublished - 1 Jan 2017
Event1st International Conference on Cyber Security Cryptography and Machine Learning, CSCML 2017 - Beer-Sheva, Israel
Duration: 29 Jun 201730 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10332 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Conference on Cyber Security Cryptography and Machine Learning, CSCML 2017

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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