A Machine Learning Framework for Detecting and Preventing Cyber‐Attacks in Industrial Cyber‐Physical Systems

ABSTRACT The increasing adoption of cyber‐physical systems (CPS) in Industry 4.0 has heightened vulnerability to cyber threats. This study proposes a machine learning–based intrusion detection framework, DBID‐Net, to effectively identify and prevent attacks in CPS environments. The framework integrates advanced modules for data handling and threat analysis, employing AMMIS for data cleaning, ADASYN for data augmentation, CESF for feature extraction, and the Whale Hopper Optimization Algorithm (WHOA) for optimal feature selection. A bidirectional learning model is used for intrusion detection, with WHOA further enhancing scalability and adaptive attack mitigation. Experimental results show that DBID‐Net achieves an F 1‐score of 0.988, a sensitivity of 0.984, a specificity of 0.983, and an accuracy of 0.9916. These findings demonstrate that DBID‐Net offers a robust and scalable solution for securing CPS infrastructures against evolving cyber threats in Industry 4.0.

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Publication Details

Journal
Engineering Reports
Published
2025-12-30
DOI
https://doi.org/10.1002/eng2.70520
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
0.00
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article

A Machine Learning Framework for Detecting and Preventing Cyber‐Attacks in Industrial Cyber‐Physical Systems

Anurag Kumar Sinha, Rejuwan Shamim, Mohammad Nadeem Ahmed, Karan Dixit et al.
Engineering Reports
Smart Grid Security and Resilience
article

A Machine Learning Framework for Detecting and Preventing Cyber‐Attacks in Industrial Cyber‐Physical Systems

Anurag Kumar Sinha, Rejuwan Shamim, Mohammad Nadeem Ahmed, Karan Dixit, Mohammad Rashid Hussain, Mihika Mahendra, Vyom Modi, Trupti Mohota, Mazar Shah, Satyam Solanki, Syed Immamul Ansarullah, Bhaskar, G. Madhukar Rao, Tanish Ranjan, Mini Kumari
article en

Abstract

ABSTRACT The increasing adoption of cyber‐physical systems (CPS) in Industry 4.0 has heightened vulnerability to cyber threats. This study proposes a machine learning–based intrusion detection framework, DBID‐Net, to effectively identify and prevent attacks in CPS environments. The framework integrates advanced modules for data handling and threat analysis, employing AMMIS for data cleaning, ADASYN for data augmentation, CESF for feature extraction, and the Whale Hopper Optimization Algorithm (WHOA) for optimal feature selection. A bidirectional learning model is used for intrusion detection, with WHOA further enhancing scalability and adaptive attack mitigation. Experimental results show that DBID‐Net achieves an F 1‐score of 0.988, a sensitivity of 0.984, a specificity of 0.983, and an accuracy of 0.9916. These findings demonstrate that DBID‐Net offers a robust and scalable solution for securing CPS infrastructures against evolving cyber threats in Industry 4.0.

Engineering ReportsVol. 8(1)
Chandigarh University (IN), Lovely Professional University (IN), University of Kashmir (IN), Institute of Management Technology (IN), Kardan University (AF), Jharkhand Rai University (IN), Marwadi Education Foundation (IN), Maharishi University of Management and Technology (IN), Raisoni Group of Institutions (IN), Central University of Kashmir (IN), Institute of Chartered Financial Analysts of India (IN), King Khalid University (SA), Koneru Lakshmaiah Education Foundation (IN), Bharati Vidyapeeth (Deemed to be University) (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 35%
Smart Grid Security and Resilience
DOIOpen Access PDFOpenAlex
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