A Deep Learning Approach for Intrusion Detection System in Industry Network

Network has brought convenience to the world by allowing flexible transformation of data, but it also exposes a high number of vulnerabilities. A Network Intrusion Detection System (NIDS) helps system and network administrators to detect network security breaches in their organizations. Identifying anonymous and new attacks is one of the main challenges in IDSs researches. Deep learning (2010’s), which is a subfield of machine learning (1980’s), is concerned with algorithms that are based on the structure and function of brain called artificial neural networks. The progression on such learning algorithms may improve the functionality of IDS especially in Industrial Control Systems to increase its detection rate on unknown attacks. In this work, we propose a deep learning approach to implement an effective and enhanced IDS for securing industrial network. Keywords—Intrusion Detection System, Deep Learning, SCADA, Modbus, Industrial Control Systems, Artificial Neural Networks.