The escalating reliance on Linear Time-Invariant (LTI) systems in critical infrastructure, industrial control systems (ICS), and interconnected networks has amplified their vulnerability to sophisticated cyberattacks, particularly those designed to be stealthy. These attacks, characterized by their ability to manipulate system dynamics subtly, pose a significant threat as they can evade traditional detection mechanisms, leading to catastrophic failures and operational disruptions. This research introduces a novel, multi-layered monitoring framework aimed at bolstering the cybersecurity of LTI systems against such stealthy attacks. Recognizing the limitations of conventional security paradigms that often fail to account for the unique dynamic behaviors of LTI systems, this framework synergistically integrates hybrid anomaly detection, adaptive thresholding, precise attack localization and identification, dynamic resilient control adaptation, and real-time interactive visualization.

The core of the proposed framework lies in its hybrid anomaly detection approach, which combines the precision of model-based residual generation using Kalman filters with the pattern recognition capabilities of deep learning autoencoders. This integration enables the detection of subtle anomalies that might otherwise be masked by normal system variations, thereby significantly enhancing the sensitivity and accuracy of attack detection. Adaptive thresholding, a critical component, dynamically adjusts detection thresholds in response to real-time system states and environmental fluctuations, effectively minimizing false alarms and improving the detection of attacks that adapt their behavior to system responses.

To facilitate rapid and effective incident response, the framework incorporates advanced attack localization and identification modules. These modules leverage graph-theoretic methods and correlation analysis to pinpoint the specific components or sensors under attack, while machine learning algorithms classify the nature and type of the attack. Furthermore, the framework features a resilient control adaptation mechanism that dynamically modifies control parameters in real-time to counteract the effects of detected attacks, ensuring system stability and operational continuity.