International Journal of Scientific Research and Engineering Development

This study presented an AI augmented network security framework designed to involve ML, AI and error-correcting codes (ECCs) to help mitigate attacks through new intrusion detection and data security in the IoT environment. Using deep-learning models including convolutional neural networks (CNNs) and adaptive particle swarm optimization (APSO), the authors sought to facilitate rapid threat identification in real time. In addition, the use of ECCs like Reed-Solomon and Turbo Codes were used to minimize errors in transmission and maintain integrity in wireless environments. The graduated experimental evaluations on standard IoT datasets, NSL-KDD and CICIDS-2017, resulted in improved detection rates +7.64%, decreased false positive rates -15%, and improved transmission reliability +30% compared with conventional security approaches.

Dr.Sattaru Janardhana Rao,"AI-Driven Intrusion Detection and Error Control Mechanisms" International Journal of Scientific Research and Engineering Development, V9(3): Page(1075-1082) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.