Enhancing Security in Heterogeneous IoT Networks through Intelligent Identification Systems

Abstract

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 identify
ing external threats in IoT environmental infrastructure showcased its potential as a robust
and scalable security solution for protecting IoT networks against diverse cyber threats.