Mitigating Cache Pollution Attack Using Deep Learning in Named Data Networking (NDN)

With the explosive growth of Internet traffic, Internet communication pays more attention to the data itself rather than where it is physically located. To better cope with Internet usage, a revolutionized shift from host-centric end-to-end communication to receiver-driven content retrieval through Information-Centric Networking (ICN) with Named Data Networking (NDN) has emerged as a big player in this new paradigm. New capabilities such as caching everywhere have made it more challenging to protect all caches from new breed of security issues especially Cache Pollution Attack (CPA). This paper presents a summary of Caching security challenges in NDN but focuses mainly on CPA. The adaptation of Machine Learning (ML) based technology is a promising emerging and proven in experimental-based simulation using ndnSIM. We show that our Python-based DL algorithm can improve the detection of CPA and outperforms some state-of-the-art techniques that mostly works on numerical counters and probability-based algorithm.