A differential privacy based multi-stage network fingerprinting deception game method

Abstract

As an active deception defense mechanism, network fingerprint obfuscation plays an important role in deceiving the attacker's fingerprinting actions. However, the existing methods show increasing limitations in coping with strategic multi-stage fingerprinting behavior in resource constrained situations. In order to solve this problem, we propose a Differential Privacy based Multi-stage fingerprinting deception Game method (DPMG). DPMG uses the multi-stage game model and the belief update mechanism to give a better description on the continuous fingerprinting interaction process. Besides, the equilibrium existence of such a game model is analyzed. Then a defender strategy optimization mechanism under the resource constraints is designed and a Recursive Algorithm Solving Equilibrium algorithm (RASE) to solve the equilibrium strategy is implemented. Experiment results show that DPMG can deceive the network fingerprinting behavior effectively under the defense resources constraints, and scale well to different network size.