A Novel Fully Convolutional Neural Network Approach For Detection and Classification of Attacks on Industrial IoT Devices in Smart Manufacturing Systems
Mohammad Shahin, F. Frank Chen, Hamed Bouzary, Ali Hosseinzadeh, Rasoul Rashidifar · Research Square · 2022
Abstract Recently, Internet of things (IoT) devices have been widely implemented and technologically advanced in manufacturing settings to monitor, collect, exchange, analyze, and deliver data. However, this transition has increased the risk of cyberattacks, exponentially. Subsequently, developing effective intrusion detection systems based on deep learning algorithms has proven to become a reliable intelligence tool to protect Industrial IoT devices against cyber threats. This paper presents the implementation of two different classifications and detection utilizing the Long Short-Term Memory (LSTM) architecture to address cyber-security concerns on three benchmark Industrial IoT datasets (BoT-IoT, UNSW-NB15, and TON-IoT) which take advantage of various deep learning algorithms. An overall analysis of the performance of the proposed models is provided. Augmenting the LSTM with Convolutional Neural Network (CNN) and Fully Convolutional Neural Network (FCN) achieves state-of-the-art performance in detecting cybersecurity threats.