Ai Techniques For Robust Data Integrity And Security In Adhoc Networks

Abstract

Ad-hoc networks are supported by an AI-based framework to enhance robustness as well as secure data transmissions. The Artificial Intelligence framework is made for getting robust and secure ways for data transfer through ad hoc networks. It combines reinforcement learning for optimizing routing in dynamic environments, supervised learning for intrusion detection, and resource management for energy efficiency and improvement in network lifetime. Changes in routing and security according to different conditions like node mobility, traffic patterns, detection of security anomalies will also be done along with such intelligent techniques. Furthermore, advanced techniques for anomaly detection will counteract black hole and denial-of-service attacks. Besides this load distribution and bandwidth allocation would also be performed dynamically for better performance in the system. Experimental results showed enormous improvements over traditional methods in terms of packet delivery ratio, latency, and security resilience of dynamic ad hoc communication.