CYBER-ATTACK DETECTION SYSTEM FOR CYBER-PHYSICAL SYSTEMS USING MACHINE LEARNING-BASED ANOMALY DETECTION
DOI:
https://doi.org/10.29121/ijetmr.v13.i4.2026.1769Keywords:
Cyber-Physical Systems, Cyber Attack Detection, Anomaly Detection, Machine Learning, Intrusion Detection, Smart SystemsAbstract
Cyber-Physical Systems (CPS) are increasingly deployed in critical domains such as smart grids, industrial automation, healthcare, and transportation, where the integration of computational and physical components introduces significant security challenges. These systems are highly vulnerable to cyber-attacks such as false data injection, denial-of-service, and stealthy manipulation, which can lead to severe physical and economic consequences. Traditional security mechanisms are insufficient due to the dynamic and heterogeneous nature of CPS environments [1], [2].
This paper proposes a conceptual machine learning-based cyber-attack detection framework tailored for CPS environments. The framework focuses on real-time anomaly detection by analyzing multi-source data streams from sensors, network traffic, and control signals. A hybrid detection approach is designed that combines statistical feature analysis and supervised learning models to identify deviations from normal system behavior. The proposed model introduces a risk scoring mechanism that evaluates system states based on behavioral inconsistencies, enabling early detection of potential threats.
The framework emphasizes scalability, adaptability, and low-latency detection, which are critical for real-time CPS applications. Unlike conventional signature-based systems, the proposed approach is capable of detecting unknown and zero-day attacks. The study provides a structured design and mathematical formulation for anomaly scoring and decision-making processes, making it suitable for further implementation and validation in real-world CPS scenarios [3].
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