Detecting Cyber-attacks in the Industrial Internet of Things using a Hybrid Deep Random Neural Network

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Mrunal K. Pathak, Arti Bang, Ranjit M. Gawande, Archana S. Banait, G. B. Sambare, Ashfaq Amir Shaikh


Critical infrastructure now faces greater vulnerabilities and a higher risk of cyberattacks as a result of the (IIoT) quick expansion. The security and dependability of industrial systems must be ensured by identifying and thwarting these threats. In this paper, we use a hybrid approach of deep learning and RNN called hybrid deep random neural network (HDRNN) to offer a novel method of identifying cyber-attacks in the IIoT.The proposed HDRNN model combines the benefits of random neural networks with deep learning to improve the detection of IIoT cyberattacks. The deep learning component makes use of deep neural networks' capacity to extract intricate features from unstructured data, while the random neural network component offers robustness and adaptability to manage changing attack patterns.Realistic threats and benchmark datasets such as UNSW-NB15and DS2OS are used in experimental evaluations. High accuracy, precision, and recall rates are attained by the model, which successfully detects a variety of assaults including infiltration, data manipulation, and denial of service.The suggested HDRNN model offers a promising approach for improving the security of IIoT systems by precisely identifying cyber-attacks in real-time. The model's hybrid nature enables enhanced detection capabilities, adaptability to changing attack patterns, and a reduction in false positives, enabling efficient threat mitigation and protecting crucial infrastructure in the IIoT context.

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Author Biography

Mrunal K. Pathak, Arti Bang, Ranjit M. Gawande, Archana S. Banait, G. B. Sambare, Ashfaq Amir Shaikh

Dr. Mrunal K. Pathak

2Arti Bang

3Dr. Ranjit M. Gawande

4Prof. Archana S. Banait

5Dr. G. B. Sambare

6Dr Ashfaq  Amir Shaikh

Assistant Professor, Department of Information Technology, AISSMS Institute of Information Technology, Savitribai Phule Pune University, Pune, India,

2Department of Electronics and Telecommunication, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India. Email:

3Asst. Prof., Department of Computer Engineering, Matoshri College of Engineering & Research Centre Nashik, affiliated to Savitribai Phule Pune University., M.S. India, Email:

4Department of Computer Engineering, MET's Institute of Engineering, Bhujbal Knowledge City, Nashik (SPPU), Maharashtra, India. Email:

5Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India. Email:

6PhD Computer Engineering, Assistant Professor Information Technology, M. H. Saboo Siddik College of Engineering, Mumbai, India. Email:


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