Title :
Mapping evidence graphs to attack graphs
Author :
Changwei Liu ; Singhal, Achintya ; Wijesekera, Duminda
Author_Institution :
Comput. Sci. Dept., George Mason Univ., Fairfax, VA, USA
Abstract :
Attack graphs compute potential attack paths from a system configuration and known vulnerabilities of a system. Evidence graphs model intrusion evidence and dependencies among them. In this paper, we show how to map evidence graphs to attack graphs. This mapping is useful for application of attack graphs and evidence graphs for forensic analysis. In addition to helping to refine attack graphs by using known sets of dependent attack evidence, important probabilistic information contained in evidence graphs can be used to compute or refine potential attack success probabilities obtained from repositories like CVSS. Conversely, attack graphs can be used to add missing evidence or remove irrelevant evidence trails to build a complete evidence graph. We illustrated the mapping by using a database attack as a case study.
Keywords :
digital forensics; graph theory; probability; CVSS; attack graphs; attack paths; database attack; dependent attack evidence; evidence graph mapping; evidence graph model intrusion evidence; forensic analysis; potential attack success probability; probabilistic information; Databases; Forensics; IP networks; Measurement; Probabilistic logic; Servers; Workstations; attack graphs; attack success probabilities; evidence graphs; evidence probabilities; mapping algorithm;
Conference_Titel :
Information Forensics and Security (WIFS), 2012 IEEE International Workshop on
Conference_Location :
Tenerife
Print_ISBN :
978-1-4673-2285-0
Electronic_ISBN :
978-1-4673-2286-7
DOI :
10.1109/WIFS.2012.6412636