Early detection and mitigation of cache-based attacks in IoT-NDN

Named Data Networking (NDN) has been identified as a key paradigm for enhancing data-centric communication, particularly in the Internet of Things (IoT) to address the scalability issues in existing solutions. Caching in NDN is a crucial mechanism for improving network performance by reducing latency and conserving bandwidth. However, it also introduces significant security challenges, making the system vulnerable to attacks like Cache Pollution Attacks and Side-Channel Timing Attacks, which can undermine data integrity and lead to denial-of-service scenarios. Traditional caching strategies, such as the Least Recently Used policy, predominantly rely on data usage frequency and often overlook critical factors including content popularity and data freshness. This oversight leaves the cache susceptible to exploitation, as it cannot effectively distinguish between valuable content and malicious requests. This paper proposes a cache management mechanism that integrates metrics such as content popularity and data freshness, facilitating early detection and mitigation of malicious activities while maximizing cache efficiency. Our results demonstrate a 96% success rate in early detection and mitigation, significantly improving the security and reliability of the IoT-NDN ecosystem.