Enhancing Security in Cyber-Physical Critical Infrastructures A Focus on Detecting Integrity Attacks through Ensemble Modeling

Abstract

Improving the safety of cyber-physical key assets is very important to keep their processes safe from attacks on their stability. In this study, we suggest a new way to find these kinds of threats using ensemble modeling methods. Integrity attacks are a big problem for these systems because they can change or control important data, which can have very bad results. Because these threats are so complex, traditional security measures often fail to spot them. This shows how important it is to have more advanced monitoring systems. The suggested method uses ensemble modeling, which blends several machine learning techniques to make identification more accurate. Ensemble modeling has shown promise in a number of defense contexts, providing resilience against different types of attacks. Ensemble approaches are good at finding integrity attacks that single models might miss because they use a variety of models, each with its own pros and cons. The study also talks about how different datasets can be used to teach ensemble models so that they can find a lot of different attack patterns. The study also looks at how to improve the ensemble's ability to find small changes from normal behavior by adding anomaly detection methods like Isolation Forest and Support Vector Machines (SVM). In general, this paper gives a complete plan for using ensemble models to make cyber-physical key systems safer. The suggested method aims to make these systems more resistant to integrity threats by using a variety of datasets and monitoring techniques. This will make sure that they work reliably and securely.