@inproceedings{f3b0ba1cad244d2aa352cb491951d0cd,
title = "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.",
keywords = "deep learning, iiot, intrusion, intrusion detection, mlp",
author = "Alani, {Mohammed M.} and Ernesto Damiani and Uttam Ghosh",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 42nd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2022 ; Conference date: 10-07-2022 Through 13-07-2022",
year = "2022",
doi = "10.1109/ICDCSW56584.2022.00040",
language = "British English",
series = "Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops, ICDCSW 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "169--174",
booktitle = "Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops, ICDCSW 2022",
address = "United States",
}
