Hybrid-Based Timing Attack for Path Inference in Named Data Networking

The vulnerability of Named Data Network (NDN) causes a series of privacy problems. Path inference provides network privacy protection and network security improvement in NDN via obtaining the content transmission paths. The traditional path inference methods ignore the influence of in-network cache, leading to a lacking consideration of the confusion issue between content sources and cached copies. To facilitate the effectiveness and robustness of the path inference, we have proposed a novel method that reduces the influence of incomplete and misleading information. The proposed method consists of two parts. First, the hybrid attack method based on hop count variation is used for collecting complete request feedback information and merging all information to infer paths. Second, the benefit-driven evolutionary game is adopted to promote the cooperation of the timing attack and the cache pollution attack. The combination of the two types of attacks alleviates the restriction of in-network cache. The simulations in ndnSIM indicate that our proposed method infers paths with higher precision, recall, and F1-Score compared to other advanced methods.