NARAHARISETTY SHASHANK REDDY , NAGARAJU P 01 2 Cyberattacks on the Internet of Things IoT are growing at an alarming rate as devices applications and communication networks are becoming increasingly connected and integrated When attacks on IoT networks go undetected for longer periods it affects availability of critical systems for end users increases the number of data breaches and identity theft drives up the costs and impacts the revenue It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense In this paper we develop an intelligent intrusiondetection system tailored to the IoT environment Specifically we use a deeplearning algorithm to detect malicious traffic in IoT networks The detection solution provides security as a service and facilitates interoperability between various network communications protocols used in IoT We evaluate our proposed detection framework using both realnetwork traces for providing a proof of concept and using simulation for providing evidence of its scalability Our experimental results confirm that the proposed intrusiondetection system can detect realworld intrusions effectively Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026