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

被引：19

作者：

Alani, Mohammed M. ^{[1
]}

Damiani, Ernesto ^{[2
]}

Ghosh, Uttam ^{[3
]}

机构：

[1] Seneca Coll Appl Arts & Technol, Sch IT Adm & Secur, Toronto, ON, Canada

[2] Khalifa Univ, Ctr Cyber Phys Syst, Abu Dhabi, U Arab Emirates

[3] Vanderbilt Univ, 221 Kirkland Hall, Nashville, TN 37235 USA

来源：

2022 IEEE 42ND INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS WORKSHOPS (ICDCSW) | 2022年

关键词：

iiot; intrusion; intrusion detection; deep learning; mlp;

D O I：

10.1109/ICDCSW56584.2022.00040

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 paper, 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.

引用

页码：169 / 174

页数：6

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