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Article overview
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Optimal Decoy Resource Allocation for Proactive Defense in Probabilistic Attack Graphs | Haoxiang Ma
; Shuo Han
; Nandi Leslie
; Charles Kamhoua
; Jie Fu
; | Date: |
3 Jan 2023 | Abstract: | This paper investigates the problem of synthesizing proactive defense systems
in which the defender can allocate deceptive targets and modify the cost of
actions for the attacker who aims to compromise security assets in this system.
We model the interaction of the attacker and the system using a formal security
model -- a probabilistic attack graph. By allocating fake targets/decoys, the
defender aims to distract the attacker from compromising true targets. By
increasing the cost of some attack actions, the defender aims to discourage the
attacker from committing to certain policies and thereby improve the defense.
To optimize the defense given limited decoy resources and operational
constraints, we formulate the synthesis problem as a bi-level optimization
problem, while the defender designs the system, in anticipation of the
attacker’s best response given that the attacker has disinformation about the
system due to the use of deception. Though the general formulation with
bi-level optimization is NP-hard, we show that under certain assumptions, the
problem can be transformed into a constrained optimization problem. We proposed
an algorithm to approximately solve this constrained optimization problem using
a novel incentive-design method for projected gradient ascent. We demonstrate
the effectiveness of the proposed method using extensive numerical experiments. | Source: | arXiv, 2301.01336 | Services: | Forum | Review | PDF | Favorites |
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