Enhancing Security in Heterogeneous IoT Networks through Intelligent Identification Systems
Journal of VLSI Circuits and Systems · 2025
This research concentrates on enhancing unauthorized access identification in Internet of Things (IoT) networks by merging antcolony optimization (ACO) with CNN to create a more accurate and efficient security system.As IoT eco framework grows, they increasingly become targets for sophisticated cyberattacks, which exploit their distributed nature and limited computational resources.To address these vulnerabilities, the proposed approach uses ACO to optimize feature compilation, minimizing data complexity and improving manipulates efficiency.These elected features are then analyzed by a CNN model, which excels in identifying complex patterns and determining anomalies with high accuracy.By integrating ACO and CNN, this hybrid structure achieves both high identification accuracy and adaptability to new and evolving threats.The effectiveness of this system in identifying external threats in IoT environmental infrastructure showcased its potential as a robust and scalable security solution for protecting IoT networks against diverse cyber threats.