Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods
Abstract Industrial control systems (ICS) are crucial for automating and optimizing industrial operations but are increasingly vulnerable to cyberattacks due to their interconnected nature. High-dimensional ICS datasets pose challenges for effective anomaly detection and classification. This study a...
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| Format: | Article |
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Springer
2025-05-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00827-2 |
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| author | Saiprasad Potharaju Swapnali N. Tambe G. Madhukar Rao M. V. V. Prasad Kantipudi Kalyan Devappa Bamane Mininath Bendre |
| author_facet | Saiprasad Potharaju Swapnali N. Tambe G. Madhukar Rao M. V. V. Prasad Kantipudi Kalyan Devappa Bamane Mininath Bendre |
| author_sort | Saiprasad Potharaju |
| collection | DOAJ |
| description | Abstract Industrial control systems (ICS) are crucial for automating and optimizing industrial operations but are increasingly vulnerable to cyberattacks due to their interconnected nature. High-dimensional ICS datasets pose challenges for effective anomaly detection and classification. This study aims to enhance ICS security by improving attack detection through an optimized feature selection framework that balances dimensionality reduction and classification accuracy. The study utilizes the HAI dataset, comprising 54,000 time series records with 225 features representing normal and anomalous ICS behaviors. A hybrid feature selection approach integrating wrapper and filter methods was employed. Initially, a Genetic Algorithm (GA) identified 118 relevant features. Further refinement was conducted using filter-based methods—Symmetrical Uncertainty (SU), Information Gain (IG), and Gain Ratio (GR)—leading to a final subset of 104 optimal features. These features were used to train classification models (Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)) with a 70:30 train-test split and tenfold cross-validation. The proposed feature selection method significantly improved classification accuracy, achieving 98.86% (NB), 99.91% (RF), and 97.97% (SVM). Compared to the full dataset (225 features), which yielded 97.51%, 99.93%, and 96.17%, respectively, our optimized feature subset maintained or enhanced classification performance while reducing computational complexity. This research demonstrates the effectiveness of a hybrid feature selection approach in improving ICS anomaly detection. By reducing feature dimensionality without compromising accuracy, the proposed method enhances ICS security, offering a scalable and efficient solution for real-time attack detection. |
| format | Article |
| id | doaj-art-77bf3b6c1cfe4c9c9ca7e470466e2c43 |
| institution | DOAJ |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-77bf3b6c1cfe4c9c9ca7e470466e2c432025-08-20T03:09:20ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-05-0118112310.1007/s44196-025-00827-2Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter MethodsSaiprasad Potharaju0Swapnali N. Tambe1G. Madhukar Rao2M. V. V. Prasad Kantipudi3Kalyan Devappa Bamane4Mininath Bendre5Symbiosis Institute of Technology, Symbiosis International (Deemed University)Department of Information Technology, K. K.Wagh Institute of Engineering Education and ResearchDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment of Electronics and Telecommunication Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University)Department of Computer Engineering, D Y Patil College of EngineeringDepartment of Computer Engineering, Pravara Rural Engineering CollegeAbstract Industrial control systems (ICS) are crucial for automating and optimizing industrial operations but are increasingly vulnerable to cyberattacks due to their interconnected nature. High-dimensional ICS datasets pose challenges for effective anomaly detection and classification. This study aims to enhance ICS security by improving attack detection through an optimized feature selection framework that balances dimensionality reduction and classification accuracy. The study utilizes the HAI dataset, comprising 54,000 time series records with 225 features representing normal and anomalous ICS behaviors. A hybrid feature selection approach integrating wrapper and filter methods was employed. Initially, a Genetic Algorithm (GA) identified 118 relevant features. Further refinement was conducted using filter-based methods—Symmetrical Uncertainty (SU), Information Gain (IG), and Gain Ratio (GR)—leading to a final subset of 104 optimal features. These features were used to train classification models (Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)) with a 70:30 train-test split and tenfold cross-validation. The proposed feature selection method significantly improved classification accuracy, achieving 98.86% (NB), 99.91% (RF), and 97.97% (SVM). Compared to the full dataset (225 features), which yielded 97.51%, 99.93%, and 96.17%, respectively, our optimized feature subset maintained or enhanced classification performance while reducing computational complexity. This research demonstrates the effectiveness of a hybrid feature selection approach in improving ICS anomaly detection. By reducing feature dimensionality without compromising accuracy, the proposed method enhances ICS security, offering a scalable and efficient solution for real-time attack detection.https://doi.org/10.1007/s44196-025-00827-2Feature selectionICSGenetic AlgorithmFilter-based methodsEmbedded methodsClassification |
| spellingShingle | Saiprasad Potharaju Swapnali N. Tambe G. Madhukar Rao M. V. V. Prasad Kantipudi Kalyan Devappa Bamane Mininath Bendre Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods International Journal of Computational Intelligence Systems Feature selection ICS Genetic Algorithm Filter-based methods Embedded methods Classification |
| title | Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods |
| title_full | Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods |
| title_fullStr | Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods |
| title_full_unstemmed | Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods |
| title_short | Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods |
| title_sort | min3gisg a synergistic feature selection framework for industrial control system security with the integrating genetic algorithm and filter methods |
| topic | Feature selection ICS Genetic Algorithm Filter-based methods Embedded methods Classification |
| url | https://doi.org/10.1007/s44196-025-00827-2 |
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