Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1958
Title: Automated generation of attack graphs using NVD
Authors: Aksu, M. Uğur
Bıçakcı, Kemal
Dilek, M. H.
Özbayoğlu, Ahmet Murat
Tatlı, E. İ.
Keywords: Network security
Intrusion detection
alert correlation
Issue Date: 2018
Publisher: Association for Computing Machinery, Inc.
Source: Aksu, M. U., Bicakci, K., Dilek, M. H., & Ozbayoglu, A. M. (2018, March). Automated Generation of Attack Graphs Using NVD. In Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy (pp. 135-142). ACM.
Abstract: Today’s computer networks are prone to sophisticated multi-step, multi-host attacks. Common approaches of identifying vulnerabilities and analyzing the security of such networks with naive methods such as counting the number of vulnerabilities, or examining the vulnerabilities independently produces incomprehensive and limited security assessment results. On the other hand, attack graphs generated from the identified vulnerabilities at a network illustrate security risks via attack paths that are not apparent with the results of the primitive approaches. One common technique of generating attack graphs requires well established definitions and data of prerequisites and postconditions for the known vulnerabilities. A number of works suggest prerequisite and postcondition categorization schemes for software vulnerabilities. However, generating them in an automated way is an open issue. In this paper, we first define a model that evolves over the previous works to depict the requirements of exploiting vulnerabilities for generating attack graphs. Then we describe and compare the results of two different novel approaches (rule-based and machine learning-employed) that we propose for generating attacker privilege fields as prerequisites and postconditions from the National Vulnerability Database (NVD) in an automated way. We observe that prerequisite and postcondition privileges can be generated with overall accuracy rates of 88,8 % and 95,7 % with rule-based and machine learning-employed (Multilayer Perceptron) models respectively.
Description: 8th ACM Conference on Data and Application Security and Privacy (2018 : Tempe; United States)
URI: https://dl.acm.org/citation.cfm?doid=3176258.3176339
https://hdl.handle.net/20.500.11851/1958
ISBN: 978-145035632-9
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

11
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

12
checked on Sep 24, 2022

Page view(s)

54
checked on Dec 5, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.