Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 63Citation - Scopus: 107Research Article Energy Consumption of On-Device Machine Learning Models for IoT Intrusion Detection(Elsevier, 2023-04) Tekin, Nazli; Acar, Abbas; Aris, Ahmet; Uluagac, A. Selcuk; Gungor, Vehbi CagriRecently, Smart Home Systems (SHSs) have gained enormous popularity with the rapid development of the Internet of Things (IoT) technologies. Besides offering many tangible benefits, SHSs are vulnerable to attacks that lead to security and privacy concerns for SHS users. Machine learning (ML)-based Intrusion Detection Systems (IDS) are proposed to address such concerns. Conventionally, ML models are trained and tested on computationally powerful platforms such as cloud services. Nevertheless, the data shared with the cloud is vulnerable to privacy attacks and causes latency, which decreases the performance of real-time applications like intrusion detection systems. Therefore, on-device ML models, in which the user data is kept locally, have emerged as promising solutions to ensure the security and privacy of the data for real-time applications. However, performing ML tasks requires high energy consumption. To the best of our knowledge, no study has been conducted to analyze the energy consumption of ML-based IDS. Therefore, in this paper, we perform a comparative analysis of on-device ML algorithms in terms of energy consumption for IoT intrusion detection applications. For a thorough analysis, we study the training and inference phases separately. For training, we compare the cloud computing-based ML, edge computing-based ML, and IoT device-based ML approaches. For the inference, we evaluate the TinyML approach to run the ML algorithms on tiny IoT devices such as Micro Controller Units (MCUs). Comparative performance evaluations show that deploying the Decision Tree (DT) algorithm on-device gives better results in terms of training time, inference time, and power consumption.Article Citation - WoS: 13Citation - Scopus: 15Comparative Analysis of Dimensionality Reduction Techniques for Cybersecurity in the SwaT Dataset(Springer, 2023-07-08) Bozdal, Mehmet; Ileri, Kadir; Ozkahraman, AliThe Internet of Things (IoT) has revolutionized the functionality and efficiency of distributed cyber-physical systems, such as city-wide water treatment systems. However, the increased connectivity also exposes these systems to cybersecurity threats. This research presents a novel approach for securing the Secure Water Treatment (SWaT) dataset using a 1D Convolutional Neural Network (CNN) model enhanced with a Gated Recurrent Unit (GRU). The proposed method outperforms existing methods by achieving 99.68% accuracy and an F1 score of 98.69%. Additionally, the paper explores dimensionality reduction methods, including Autoencoders, Generalized Eigenvalue Decomposition (GED), and Principal Component Analysis (PCA). The research findings highlight the importance of balancing dimensionality reduction with the need for accurate intrusion detection. It is found that PCA provided better performance compared to the other techniques, as reducing the input dimension by 90.2% resulted in only a 2.8% and 2.6% decrease in the accuracy and F1 score, respectively. This study contributes to the field by addressing the critical need for robust cybersecurity measures in IoT-enabled water treatment systems, while also considering the practical trade-off between dimensionality reduction and intrusion detection accuracy.Article Citation - WoS: 47Citation - Scopus: 68Artificial Intelligence Based Intrusion Detection System for IEC 61850 Sampled Values Under Symmetric and Asymmetric Faults(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Ustun, Taha Selim; Hussain, S. M. Suhail; Yavuz, Levent; Onen, AhmetModern power systems require increased connectivity to implement novel coordination and control schemes. Wide-spread use of information technology in smartgrid domain is an outcome of this need. IEC 61850-based communication solutions have become popular due to a myriad of reasons. Object-oriented modeling capability, interoperable connectivity and strong communication protocols are to name a few. However, power system communication infrastructure is not well-equipped with cybersecurity mechanisms for safe operation. Unlike online banking systems that have been running such security systems for decades, smartgrid cybersecurity is an emerging field. A recent publication aimed at equipping IEC 61850-based communication with cybersecurity features, i.e. IEC 62351, only focuses on communication layer security. To achieve security at all levels, operational technology-based security is also needed. To address this need, this paper develops an intrusion detection system for smartgrids utilizing IEC 61850's Sampled Value (SV) messages. The system is developed with machine learning and is able to monitor communication traffic of a given power system and distinguish normal data measurements from falsely injected data, i.e. attacks. The designed system is implemented and tested with realistic IEC 61850 SV message dataset. Tests are performed on a Modified IEEE 14-bus system with renewable energy-based generators where different fault are applied. The results show that the proposed system can successfully distinguish normal power system events from cyberattacks with high accuracy. This ensures that smartgrids have intrusion detection in addition to cybersecurity features attached to exchanged messages.