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A deep hybrid learning model for detection of cyber attacks in industrial IoT devices.

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Názov: A deep hybrid learning model for detection of cyber attacks in industrial IoT devices.
Autori: Shahin, Mohammad, Chen, F. Frank, Hosseinzadeh, Ali, Bouzary, Hamed, Rashidifar, Rasoul
Zdroj: International Journal of Advanced Manufacturing Technology; Nov2022, Vol. 123 Issue 5/6, p1973-1983, 11p
Predmety: BLENDED learning, CYBERTERRORISM, INTRUSION detection systems (Computer security), LONG-term memory, INTELLECTUAL property theft, DEEP learning, MANUFACTURING processes
Abstrakt: With the rapid advancement of wireless technology, the problem of cybersecurity monitoring and detection of cyber-attacks has been receiving widespread attention from industry and academia. The consequences of an undetected cyber-attack in a manufacturing system are not limited to intellectual property theft and cost. It may include destroying equipment, changing product plans, or altering processes. This paper proposes a deep hybrid learning model to improve network intrusion detection systems. To this end, initially, the data set is normalized and preprocessed. Afterward, deep hybrid learning models integrating Attention-based Long Short Term Memory (ALSTM) and Fully Convolutional Neural Network (FCN) with Gradient Boosting, such as Extreme Gradient Boosting (XGBoost) and Adaptive Boost (AdaBoost) are constructed to detect anomalies in traffic data of industrial internet of things (IoT) devices, successfully. The proposed model managed to detect cybersecurity threats in seven different Industrial Internet of Things (IIoT) devices with high-performance measures. The results reveal that deep hybrid learning can ideally detect cyber-security attacks and be versatile in detecting different types of attacks. [ABSTRACT FROM AUTHOR]
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Databáza: Complementary Index
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Abstrakt:With the rapid advancement of wireless technology, the problem of cybersecurity monitoring and detection of cyber-attacks has been receiving widespread attention from industry and academia. The consequences of an undetected cyber-attack in a manufacturing system are not limited to intellectual property theft and cost. It may include destroying equipment, changing product plans, or altering processes. This paper proposes a deep hybrid learning model to improve network intrusion detection systems. To this end, initially, the data set is normalized and preprocessed. Afterward, deep hybrid learning models integrating Attention-based Long Short Term Memory (ALSTM) and Fully Convolutional Neural Network (FCN) with Gradient Boosting, such as Extreme Gradient Boosting (XGBoost) and Adaptive Boost (AdaBoost) are constructed to detect anomalies in traffic data of industrial internet of things (IoT) devices, successfully. The proposed model managed to detect cybersecurity threats in seven different Industrial Internet of Things (IIoT) devices with high-performance measures. The results reveal that deep hybrid learning can ideally detect cyber-security attacks and be versatile in detecting different types of attacks. [ABSTRACT FROM AUTHOR]
ISSN:02683768
DOI:10.1007/s00170-022-10329-6