@inproceedings{b98d461205c244b3a7b167fcdbf0c44e,
title = "Attack graph obfuscation",
abstract = "Before executing an attack, adversaries usually explore the victim{\textquoteright}s network in an attempt to infer the network topology and identify vulnerabilities in the victim{\textquoteright}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.",
author = "Hadar Polad and Rami Puzis and Bracha Shapira",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 1st International Conference on Cyber Security Cryptography and Machine Learning, CSCML 2017 ; Conference date: 29-06-2017 Through 30-06-2017",
year = "2018",
month = may,
day = "3",
doi = "10.1007/978-3-319-60080-2_20",
language = "American English",
isbn = "9783319600796",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "269--287",
editor = "Shlomi Dolev and Sachin Lodha",
booktitle = "Cyber Security Cryptography and Machine Learning - 1st International Conference, CSCML 2017, Proceedings",
address = "Germany",
}