Advancing Security in SCADA Systems: Enhanced Intrusion Detection Using Optimized Support Vector Machines and Strategic Data Management

SCADA systems play a vital role in modern critical infrastructure protection, but they are also the prime targets for a wide variety of cyber threats. This paper presents innovative, advanced intrusion detection using an Optimized Support Vector Machine framework to cater to SCADA environments. This paper stitches a complete process for SCADA data feature preprocessing, which also included some novel methods to deal with missing values, based on Gaussian Mixture Models and K-means clustering, in combination with the implementation of mean and min-max normalization to improve the quality of the provided input to the model. This research was based on these kinds of preprocessed techniques in training and optimizing the SVM for achieving high classification accuracy and robustness. Results of the repeated experiments prove that in improvements of major detection performance, the optimized SVM accuracy reached up to 95.77% in distinguishing normal operation states from potential security compromise states, presenting the present method as effective in the proliferation of resilience of SCADA systems against cyber intrusions. This work not only provides further strengthening to this ongoing effort in enhancing the security of critical infrastructures, but also serves as a steppingstone for further research towards developing an effective and scalable detection system.