An Intrusion Detection System Empowered by Deep Learning Algorithms

Abstract

The Industrial Internet of Things (IIoT) faces significant challenges in managing the increasing volume of network traffic, which calls for robust cybersecurity measures, especially in intrusion detection. An effective intrusion detection system (IDS) is essential to protect industrial systems from cyber attacks, enabling quick identification and response to security threats. However, traditional machine learning techniques have limitations in capturing complex patterns and correlations in the extensive data generated by IoT devices. Deep learning (DL) offers a promising alternative by employing multi-layer neural networks to extract hierarchical abstract features, resulting in improved accuracy and generalization. DL models handle large and complex datasets and address intricate problems, making them ideal for overcoming security challenges in cybersecurity domains. In this context, this paper explores the application of recent DL and optimization techniques in IIoT intrusion detection to develop more accurate, robust, and efficient models for threat detection. Experimental results on the recent EdgeIIoT dataset demonstrate a remarkable accuracy rate, and rapid responsiveness within our framework, effectively identifying various known attacks and affirming the proposed IDS system's feasibility.