Browsing by Author "Uluagac, A. Selcuk"

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Citation - WoS: 50
Citation - Scopus: 88
Research Article Energy Consumption of On-Device Machine Learning Models for IoT Intrusion Detection
(Elsevier, 2023) Tekin, Nazli; Acar, Abbas; Aris, Ahmet; Uluagac, A. Selcuk; Gungor, Vehbi Cagri
Recently, 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.
Citation - Scopus: 18
A Review of Moving Target Defense Mechanisms for Internet of Things Applications
(Wiley, 2020) Saputro, Nico; Tonyali, Samet; Aydeger, Abdullah; Akkaya, Kemal; Rahman, Mohammad A.; Uluagac, A. Selcuk
The chapter presents a review of proactive Moving Target Defense (MTD) paradigm and investigates the feasibility and potential of specific MTD approaches for the resource-constrained Internet of Things (IoT) applications. The aim is not only to provide taxonomy of various MTD approaches but also to advocate MTD techniques in the dynamic network domain in conjunction with the emerging Software Defined Networking (SDN) for more effective proactive IoT defense. The Internet of Battlefield Things (IoBT) and Industrial IoT (IIoT), which subject to more attacks, are identified as two critical IoT domains that can reap from the SDN-based MTD approaches. Finally, the chapter also discusses potential future research challenges of the MTD approaches in the IoT domain. © 2025 Elsevier B.V., All rights reserved.