IBPC: An Approach for Mitigation of Cache Pollution Attack in NDN using Interface-Based Popularity

The performance of Named Data Networking (NDN) depends on the caching efficiency of routers. Cache Pollution Attack (CPA) refers to colonization of unpopular contents in the Content Store (CS) of an NDN router, which leads to declined Quality of Service (QoS) in NDN. CPA has very few solutions proposed for its mitigation. Most of these solutions are based on the statistics of the router itself. However, an attacker can influence these statistics by requesting unpopular contents repeatedly. This article proposes a new parameter for the detection of CPA, which is based on the number of distinct users requesting interest packets for a content over a period of time. The local popularity of the attackers’ content does not affect the proposed approach. Results show that the proposed approach consumes less storage, reduces processing time, and more effectively mitigates the CPA, as compared to the other existing approaches.

Cache Pollution Detection Method Based on GBDT in Information-Centric Network

There is a new cache pollution attack in the information-centric network (ICN), which fills the router cache by sending a large number of requests for nonpopular content. This attack will severely reduce the router cache hit rate. Therefore, the detection of cache pollution attacks is also an urgent problem in the current information center network. In the existing research on the problem of cache pollution detection, most of the methods of manually setting the threshold are used for cache pollution detection. The accuracy of the detection result depends on the threshold setting, and the adaptability to different network environments is weak. In order to improve the accuracy of cache pollution detection and adaptability to different network environments, this paper proposes a detection algorithm based on gradient boost decision tree (GBDT), which can obtain cache pollution detection through model learning. Method. In feature selection, the algorithm uses two features based on node status and path information as model input, which improves the accuracy of the method. This paper proves the improvement of the detection accuracy of this method through comparative experiments.