Security challenges and strategies for CNN-based intrusion detection model for IoT networks

Abstract

The rapid proliferation of internet-of-things (IoT) networks has revolutionized various industries but has also exposed them to a myriad of security threats. These networks are particularly vulnerable to sophisticated cyber-attacks due to their distributed nature, resource constraints, and the diverse range of connected devices. To safeguard IoT systems, intrusion detection systems (IDS) have emerged as a critical security measure. Among these, convolutional neural network (CNN)-based models offer promising capabilities in recognizing and mitigating malicious activities within IoT environments. This paper addresses the security challenges specific to IoT networks and explores the critical aspects of identifying malicious packets that threaten their integrity. It also delves into the general challenges associated with implementing IDS in IoT settings, such as the need for real-time detection, resource efficiency, and adaptability to evolving threats. The discussion extends to potential strategies for enhancing CNN-based IDS. The paper concludes by summarizing the key findings and proposing directions for future research to overcome the identified challenges, ultimately contributing to the development of more robust and effective IDS solutions for securing IoT networks.