A Lightweight Security Scheme for Detecting Multiple Attacks in Named Data Networking

In the emerging network trends, the Named Data Networking (NDN) is threatened by many cyber-attacks and several security threats such as Cache Pollution Attacks (CPA), Cache Poisoning Attacks (PA), Interest Flooding Attacks (IFA), and Distributed Denial of Service (DDoS) attacks can severely humiliate network functioning and disrupt assistance. Detecting and analyzing these attacks in real time is crucial but tough due to the computational complexity of verifying content authenticity and integrity, mainly in high-traffic environments. Many researches made for detecting a specific type of attack rather than three. This paper gives the development of a new lightweight security scheme which is named as “DAMA-NDN” (Detection and Analysis of Multiple Attacks in NDN) that integrates probabilistic verification, lightweight cryptographic techniques, and machine learning-based anomaly detection to identify and analyze these attacks while minimizing performance bottlenecks such as latency and computational overhead. The proposed DAMA-NDN scheme will dynamically balance the trade-offs between security and efficiency by selectively verifying data packets based on traffic patterns, trust models, and content popularity. The DAMA-NDN scheme is also compared with existing detection methods, and the results show accuracy of 95.2% and lower overhead of 8.1 seconds under multiple attack conditions in NDN.