Cybersecurity for LTI Systems : Advanced Monitoring Against Stealthy Attacks
Keywords:
Cybersecurity, LTI Systems, Advanced Monitoring, Stealthy Attacks, Real-time Data Analysis, Anomaly Detection, Machine Learning AlgorithmsAbstract
In today's interconnected world, Linear Time-Invariant (LTI) systems serve as critical components in various domains, from industrial automation to critical infrastructure control. These systems are often vulnerable to a growing spectrum of cyber threats, including stealthy attacks that aim to evade traditional security measures. In response to this evolving threat landscape, this paper introduces an advanced monitoring framework for bolstering the cybersecurity of LTI systems.Our research addresses the need for proactive and adaptive security measures, going beyond conventional intrusion detection and prevention mechanisms. We propose a novel approach that combines real-time data analysis, anomaly detection, and machine learning algorithms to identify and mitigate stealthy attacks effectively. This framework not only enhances the resilience of LTI systems but also minimizes the potential damage caused by these covert threats.The key components of our advanced monitoring system include the continuous collection of system data, feature extraction, and the application of sophisticated machine learning models. By analyzing the system's behavior in real time, we can detect subtle deviations from the expected norm, which are indicative of stealthy attacks. Our approach adapts to changing attack tactics and remains effective even when attackers employ sophisticated evasion techniques.
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