Les POMDP font de meilleurs hackers: Tenir compte de l'incertitude dans les tests de penetration
This addresses the challenge of scalable and intelligent attack planning in network security, offering a novel approach that reduces reliance on costly pre-processing compared to classical methods.
The paper tackled the problem of generating automated penetration testing attacks under network uncertainty by modeling it as a partially observable Markov decision process (POMDP), and demonstrated effectiveness in runtime and solution quality on an industrial test suite.
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 network structure to the extent possible, making targeted approximations (only) where needed. Evaluating this method on a suitably adapted industrial test suite, we demonstrate its effectiveness in both runtime and solution quality.