DeepIIoT: An Explainable Deep Learning Based Intrusion Detection System for Industrial IOT

Abstract

IoT adoption is becoming widespread in different areas of applications in our daily lives. The increased reliance on IoT devices has made them a worthy target for attackers. With malicious actors targeting water treatment facilities, power grids, and power nuclear reactors, industrial IoT poses a much higher risk in comparison to other IoT application contexts. In this pa-per, we present a deep-learning based intrusion detection system for industrial IoT. The proposed system was trained and tested using the WUSTL-IIOT-2021 dataset. Testing results showed accuracy exceeding 99% with minimally low false-positive, and false-negative rates. The proposed model was explained using SHAP values.