Distributed deep learning approach for intrusion detection system in industrial control systems based on big data technique and transfer learning

ABSTRACT

Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, Big Data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. In order to improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods, Big Data techniques and deployed in a cloud environment. A variety of Machine Learning and Deep Learning algorithms, basically convolutional neural networks (CNN), have been tested to compare performance and choose the most suitable model for the classification. We test the performance of our model by using the industrial dataset SWat.

KEYWORDS:

• Intrusion detection
• industrial control systems
• machine learning
• deep learning
• transfer learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://itrust.sutd.edu.sg/testbeds/secure-water-treatment-swat/

Additional information

Notes on contributors

Ahlem Abid

Ahlem Abid is currently pursuing a PhD at the Higher Institute of Computer Science and Telecom of Hammam Sousse. Additionally, she is an active researcher affiliated with the MARS Laboratory at ISITCom, University of Sousse in Tunisia. In the domains of Artificial Intelligence (including machine learning and deep learning), Big Data Analysis and Cloud computing she contributes as a researcher with a dedicated emphasis on Intrusion Detection Systems.

Farah Jemili

Dr. Eng. Farah Jemili holds an Engineer degree (2002), a MSc degree (2004), and a PhD degree (2010) in computer science. She is currently an Assistant Professor at Higher Institute of Computer Science and Telecom of Hammam Sousse (ISITComUniversity of Sousse) and a Senior Researcher at MARS Laboratory (ISITCom-University of Sousse). She has extensive experience as a researcher in Artificial Intelligence, Big Data Analysis and Distributed Systems, with special focus on Intrusion Detection Systems. She has over 35 publications and served as reviewer for many international conferences and journals.

Ouajdi Korbaa

Ouajdi Korbaa is a full-time professor at the University of Sousse (Tunisia). He received his Engineering Diploma from the Ecole Centrale de Lille (France) in 1995 and his Masters degree in Production Engineering and Computer Science from the University of Lille (France) in the same year. He obtained his Ph.D. in Production Management, Automatic Control, and Computer Science from the University of Science and Technologies of Lille (France) in 1998 and his ‘‘Habilitation to Supervise Researches’’ degree in Computer Science from the same University in 2003. Pr. Korbaa has published around 150 research papers on Optimistation, Simultation and Modeling, Applied and Computational Mathematics, Manufacturing Engineering and Computer Engineering.