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Title |
Securing SCADA Systems: A Comprehensive Machine Learning Approach for Detecting Reconnaissance Attacks |
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Author |
Ezaz Aldahasi and Talal Alkharobi |
| Citation |
Vol. 23 No. 12 pp. 1-12 |
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Abstract |
Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats. |
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Keywords |
SCADA/ICS security; Reconnaissance attacks; Critical infrastructure; Imbalanced dataset handling techniques; Ensemble methods; Feature Engineering |
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