AI Methods for Anomaly Detection in Cyber-Physical Systems: With Application to Water and Agriculture

In today's interconnected infrastructures, Cyber-Physical Systems (CPSs) play a critical role in domains including water distribution, agricultural production, and energy management. Modern infrastructures rely on a network of cyber-physical components—mechanical actuators, electrical sensors, and internet-connected devices—to supervise and manage operational processes. However, the increasing complexity and connectivity of these systems amplify their vulnerability to cyberattacks, necessitating robust cybersecurity measures and effective Outlier Detection (OD) methods. These methods are essential to prevent infrastructure failures, reduce environmental waste, and mitigate damages caused by malicious activities. Existing approaches often lack the integration of multiple operational metrics and context-driven techniques, hampering their effectiveness in real-world scenarios. In large CPSs—comprising hundreds or thousands of sensors, actuators, PLCs, IoT devices, and complex Control and Protection Switching Gear (CPSG)—the challenge of ensuring data quality, security, and reliability is costly.

Cyberattacks frequently appear as outliers or anomalies in the data and are launched with "minimum perturbation," making their detection significantly challenging. This dissertation proposes a novel framework, multiple pipelines, and AI-based methods to develop context-driven, data-driven, and assurance-focused OD solutions. Emphasis is placed on water and agricultural systems, illustrating the proposed framework's effectiveness, particularly through enhanced decision-making, operational efficiency, and cybersecurity measures. A comprehensive survey of OD methods that employ Artificial Intelligence (AI) techniques establishes the foundational understanding of OD. This survey underscores that successful OD depends on domain knowledge, contextual factors, and assurance principles. Synthesizing these insights, the dissertation leverages synthetically generated SCADA data and GAN-produced poisoned data, as well as real-world SCADA data from Wastewater Treatment Plants (WWTPs), to identify outliers and address critical problems—such as forecasting tunnel wastewater overflows under extreme weather conditions—by applying Recurrent Neural Network (RNN)-based Deep Learning (DL) methods. Additionally, an AI-based decision support tool is introduced to detect anomalies in complex plant data and optimize operational set-points, thereby aiding Operation and Maintenance (OandM) in Water Distribution Systems (WDSs).

Similarly, in Agricultural Production Systems (APSs), which traditionally rely on reactive policies and short-term solutions, integrating advanced AI-driven OD methods provides farmers with timely, data-informed decisions that account for contextual changes resulting from outlier events. Machine Learning (ML) and DL methods measure associations, correlations, and causations among global and domestic factors, aiding in the accurate prediction of agricultural production. This contextual awareness helps manage policy, optimize resource utilization, and support precision agriculture strategies.

The main contributions of this dissertation include introducing a novel framework that integrates OD techniques with AI assurance and context-driven methodologies in CPSs; developing multiple pipelines and DL models that enhance anomaly detection, forecasting accuracy, and proactive decision support in WDSs and APSs; and demonstrating measurable improvements in cybersecurity, operational efficiency, and predictive capability using real-world and synthetic data. These efforts collectively foster more trustworthy and sustainable CPSs. Experimental results are recorded, evaluated, and discussed, revealing that these contributions bridge the gap between complex theoretical constructs and tangible real-world applications.