POMDPs Make Better Hackers: Accounting for Uncertainty in Penetration Testing

Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks. Doing so automatically allows for regular and systematic testing. A key question is how to generate the attacks. This is naturally formulated as planning under uncertainty, i.e., under incomplete knowledge about the network configuration. Previous work uses classical planning, and requires costly pre-processes reducing this uncertainty by extensive application of scanning methods. By contrast, we herein model the attack planning problem in terms of partially observable Markov decision processes (POMDP). This allows to reason about the knowledge available, and to intelligently employ scanning actions as part of the attack. As one would expect, this accurate solution does not scale. We devise a method that relies on POMDPs to find good attacks on individual machines, which are then composed into an attack on the network as a whole. This decomposition exploits netw...

• Type: Journal
• Year: 2012
• Where: AAAI
• Authors: Carlos Sarraute, Olivier Buffet, Jörg Hoffmann
• Keywords: AAAI 2012, Assessing Network Security, Intelligent Agents, Observable Markov Decision, Planning Under Uncertainty