Malicious Source Detection and Threats Mitigation in Named Data Networking Using Deep Learning
International journal of intelligent engineering and systems · 2024
Named Data Networking (NDN) is a next-generation internet architecture that routes data packets based on content identity rather than IP addresses, shifting focus from location to identity.However, because NDN routers are decentralized and have a limited capacity for data packet authentication, the networks are exposed to a variety of security risks like cache poisoning.Identifying and blocking malicious sources in NDN networks is challenging.To overcome these issues, a novel Secure Named Data networking using Deep Learning (SEND-DL) technique has been proposed to address security concerns in NDN by focusing on mitigating threats such as cache poisoning attacks and identifying malicious sources.The proposed method introduces a Feedback packet type and utilizes the Boneh-Lynn-Shacham (BLS) signature scheme for authentication, alongside Convolutional Neural Network (CNN) and Deep Belief Network (DBN) based techniques for content store cleanup and malicious source identification.The method dynamically adjusts forwarding paths, prioritizing legitimate content retrieval while blocking malicious sources.Experimental outcomes validate the efficiency of the proposed SEND-DL approach using evaluation metrics like content retrieval latency, communication overhead, cache hit rate, deployment complexity, security effectiveness, and fault tolerance which are compared to existing techniques like Q-learning based cache pollution attack mitigation approach for named data networking (Q-ICAN), Dynamic FOrest of Random Subsets based Capability enhancing Security Architecture (DFORS-CSA), and Mitigation of Sophisticated Interest flooding-based DDoS attacks in Named data networking (MISDN).The security effectiveness of the proposed SEND-DL is 6.18%, 20.06%, and 9.62% higher than the existing Q-ICAN, DFORS-CSA, and MISDN methods respectively.