Cache Poisoning Attack Detection in Vehicular Named Data Networking Using Threshold-Based Reputation Algorithm

Abstract

Cache poisoning attacks in Vehicular Named Data Networking (V-NDN) pose a serious threat by injecting false content into the Content Store, compromising network integrity. This paper proposes a threshold-based reputation algorithm to detect and mitigate such attacks in V-NDN using ndnSIM with a V2V multi-hop topology of 31 nodes across three urban road segments. The algorithm assigns reputation values to all nodes, applying penalties for malicious behavior and blocking nodes below a threshold of 0.5. Two scenarios are evaluated: an attack scenario without mitigation and an attack scenario with the proposed reputation algorithm. Results show a Detection Rate (DR) of 75%, False Positive Rate (FPR) of 0%, and False Cache Poisoning Rate (FCP) of 5.87%, with Cache Hit Ratio (CHR) improving from 28.94% to 30.99% after mitigation, confirming the algorithm's effectiveness in detecting and mitigating cache poisoning attacks in dynamic vehicular network environments.