Q-ICAN: A Q-learning based cache pollution attack mitigation approach for named data networking

Abstract

The Cache Pollution Attack (CPA) is a recent threat that poses a significant risk to Named Data Networks (NDN). This attack can impact the caching process in various ways, such as causing increased cache misses for legitimate users, delays in data retrieval, and exhaustion of resources in NDN routers. Despite the numerous countermeasures suggested in the literature for CPA, many of them have detrimental effects on the NDN components. In this paper, we introduce Q-ICAN, a novel intelligent technique for detecting and mitigating cache pollution attacks in NDN. More specifically, Q-ICAN uses Q-Learning as an automated CPA prediction mechanism. Each NDN router integrates a reinforcement learning agent that utilizes impactful metrics such as the variation of the Cache Hit Ratio (CHR) and the interest inter-arrival time to learn how to differentiate between malicious and legitimate interests. We conducted several simulations using NDNSim to assess the effectiveness of our solution in terms of Cache Hit Ratio (CHR), Average Retrieval Delay (ARD) and multiple artificial intelligence evaluation metrics such as accuracy, precision, recall, etc. The obtained results confirm that Q-ICAN detects CPA attacks with a 95.09% accuracy rate, achieves a 94% CHR, and reduces ARD by 18%. Additionally, Q-ICAN adheres to the security policy of the NDN architecture and consumes fewer resources from NDN routers compared to existing state-of-the-art solutions.

Keywords: Named data networking, Cache pollution attack, Q-learning