A Survey on Mitigation of Cache Pollution Attacks in NDN

Named Data Networking (NDN) improves data retrieval by using in-network caching, but this advantage makes it susceptible to cache pollution attacks, where malicious or irrelevant content fills caches and reduces network efficiency. This paper reviews several mitigation techniques for these attacks, grouping them into proactive, reactive, and collaborative approaches. Each strategy is assessed based on its scalability, detection accuracy, and overall impact on network performance. While some progress has been made, existing methods often struggle in large, dynamic environments, where they tend to be computationally expensive and lack adaptability. The survey identifies key research gaps, such as the need for real-time, adaptive solutions that can operate without compromising network performance. It also highlights the potential for using AI and machine learning to enhance detection accuracy and reduce false positives. Future research should focus on developing scalable, decentralized systems to strengthen the security and efficiency of NDN’s caching mechanisms.