Browsing by Author "Sahin, Ali Kivanc"

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Detecting Cyberattacks Based on Deep Neural Network Approaches in Industrial Control Systems
(Elsevier, 2025) Ayas, Selen; Ayas, Mustafa Sinasi; Cavdar, Bora; Sahin, Ali Kivanc
Historical cases demonstrate the growing cybersecurity threats associated with water distribution and treatment systems, which are essential components of infrastructure. Detecting anomalies in time series data from industrial control systems has become an important issue due to its significance. This paper proposes an anomaly detection approach that utilizes statistical measurements and the relationship between observed and predicted values of deep neural network (DNN) models. To achieve this goal, we compared several convolutional and recurrent DNN architectures, including convolutional neural network (CNN), long shortterm memory (LSTM), recurrent neural network (RNN), and gated recurrent unit (GRU) models. Our aim was to automatically learn the relationships between sensors from time series data, improve detection performance, and quickly extract long-term and short-term dependencies to help detect possible anomalies. The performances of the DNN models on two real water system datasets, Secure Water Treatment (SWaT) and Water Distribution (WADI) datasets, were analyzed. The results indicate that the GRU model is more efficient than the other models in reducing the absolute error between the predicted and observed values, when evaluated in terms of prediction performance for both datasets. Additionally, the RNN model demonstrated successful anomaly detection with high F1-score values of 0.9848 and 0.7651 for SWaT and WADI datasets. The study provides valuable information on how to secure water networks against online attacks through extensive testing and comparative evaluation.
Fine-Tuned Design of the PIDFFD2F Control Strategy for the Functional Electrical Stimulation System Using Dandelion Optimizer
(Elsevier Science Ltd, 2025) Sahin, Ali Kivanc; Ayas, Mustafa Sinasi
Functional Electrical Stimulation (FES) is a therapeutic and rehabilitative technique that uses electrical currents to stimulate nerves and muscles. This technology is vital in a wide range of medical applications, particularly for people with neurological disorders, paralysis or other conditions that affect muscle function. On the other hand, the complexity of human neuromuscular responses requires the design of a control structure that can dynamically adapt to changing conditions to ensure effective and safe stimulation. Therefore, this paper proposes proportional-integral-derivative with fractional order derivative filter plus double derivative with filter (PIDFFD2F) and proportional-integral-derivative with fractional order derivative filter (PIDFF) control structures to improve the performance of a FES system. The fine-tuning of the parameters in the proposed control structures is achieved by an efficient and effective algorithm, the Dandelion Optimizer (DO). The effectiveness of the DO-PIDFFD2F and DO-PIDFF controllers on the FES system has been thoroughly investigated through a series of tests and analyses, encompassing aspects such as transient response, Bode analysis, rejection of external disturbances, handling of measurement sensor noise, adaptability to parameter changes, responsiveness to reference changes, realistic scenario evaluations, and analysis of nonlinearity effects. The simulation results include the comparison of the proposed DO-PIDFFD2F and DO-PIDFF controllers with PIDF and PID controllers tuned using different metaheuristic algorithms from the literature. The obtained results show that the proposed DO-PIDFFD2F control technique is highly successful in terms of stability and robustness. In conclusion, this study provides comprehensive and robust results supporting the effectiveness and superiority of the DO-PIDFFD2F control method on the FES system.
OptAML: Optimized Adversarial Machine Learning on Water Treatment and Distribution Systems
(Elsevier, 2025) Ayas, Mustafa Sinasi; Kara, Enis; Ayas, Selen; Sahin, Ali Kivanc
This research presents the optimized adversarial machine learning framework, OptAML, which is developed for use in water distribution and treatment systems. Inconsideration of the physical invariants of these systems, the OptAML generates adversarial samples capable of deceiving a hybrid convolutional neural network- long short-term memory network model. The efficacy of the framework is assessed using the Secure Water Treatment (SWaT) and Water Distribution (WADI) datasets. The findings demonstrate that OptAML is capable of effectively evading rule checkers and significantly reducing the accuracy of anomaly detection frameworks in both systems. Additionally, the study investigates a defense mechanism that demonstrates enhanced robustness against these adversarial attacks and is based on adversarial training. Our results underscore the necessity for robust and flexible protection tactics and highlight the shortcomings of the machine learning-based anomaly detection systems for critical infrastructure that are currently in place.
A Reinforcement Learning Approach to Automatic Voltage Regulator System
(Pergamon-Elsevier Science Ltd, 2023) Ayas, Mustafa Sinasi; Sahin, Ali Kivanc
An Automatic Voltage Regulator (AVR) system utilized to keep the terminal voltage of a synchronous generator at the desired level has received much attention among researchers. Designing an efficient and robust control scheme for the AVR system to maintain a specified voltage level is an important research area. From the control area perspective, reinforcement learning, an adaptive optimal control method, has received increasing attention in reference tracking problems. This article discusses a reinforcement learning approach to an AVR system and its experimental validation. A deep deterministic policy gradient (DDPG) agent working in continuous-time is designed offline to improve dynamic system characteristics of the AVR system besides its robustness against load disturbance, parameter uncertainties, and reference change. In the DDPG agent design process, the limits of the produced control signal are taken into account to perform a feasible simulation similar to a real-time application. The performance of the proposed learning-based controller is analyzed in three categories: transient and steady-state responses, stability analysis, and robustness analysis against parameter uncertainties, reference change, and load disturbance. A comparison with recently published papers employing Fuzzy-PID, PID-F, (PIDNDN2)-D-lambda-N-2, PIDD2 ,and PID controllers in which various heuristic optimization algorithms were employed to optimally tune the controller parameters is made. Furthermore, to demonstrate that the behavior of the learning-based approach provides a stable and satisfactory performance, it is analyzed for a real synchronous generator connected to a 230 kV network using Matlab/Simulink environment. The results presented in this paper indicate that the proposed learning-based controller ensures the stability of the AVR system, significantly improves the regulating performance, and most impressively, is robust against parameter uncertainties, reference change, and load disturbance.