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This paper presents an intelligent and adaptive agent that employs deceptionto recognize a cyber adversary's intent. Unlike previous approaches to cyberdeception, which mainly focus on delaying or confusing the attackers, we focuson engaging with them to learn their intent. We model cyber deception as asequential decision-making problem in a two-agent context. We introducefactored finitely nested interactive POMDPs (I-POMDPx) and use this frameworkto model the problem with multiple attacker types. Our approach models cyberattacks on a single honeypot host across multiple phases from the attacker'sinitial entry to reaching its adversarial objective. The defendingI-POMDPx-based agent uses decoys to engage with the attacker at multiple phasesto form increasingly accurate predictions of the attacker's behavior andintent. The use of I-POMDPs also enables us to model the adversary's mentalstate and investigate how deception affects their beliefs. Our experiments inboth simulation and on a real host show that the I-POMDPx-based agent performssignificantly better at intent recognition than commonly used deceptionstrategies on honeypots.

Active Deception using Factored Interactive POMDPs to Recognize Cyber Attacker's Intent