Improved self adaptive honeypots capable of detecting rootkit malware

The latest trends in the development of high interaction honeypots show that adaptive honeypots, which lure attackers by changing their behavior, are a feasible solution for gathering of as much information as possible about them. Adaptive Honeypot systems based on Game theory are in a development stage and the systems created until now are focused mostly on applying game-theoretic concepts for the configuration and reciprocal actions of high-interaction honeypots [1]. The paper presents a tested proof of concept system that integrates dynamic taint analysis with an existing adaptive honeypot in order to detect the rootkit malware that the attacker installs.