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            <front>

                <journal-meta>
                                                                <journal-id>saucis</journal-id>
            <journal-title-group>
                                                                                    <journal-title>Sakarya University Journal of Computer and Information Sciences</journal-title>
            </journal-title-group>
                                        <issn pub-type="epub">2636-8129</issn>
                                                                                            <publisher>
                    <publisher-name>Sakarya University</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.35377/saucis.8.94717.1641393</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Computer Software</subject>
                                                            <subject>Software Engineering (Other)</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Bilgisayar Yazılımı</subject>
                                                            <subject>Yazılım Mühendisliği (Diğer)</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                                                            <article-title>Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks</article-title>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0001-6840-8545</contrib-id>
                                                                <name>
                                    <surname>Yıldız</surname>
                                    <given-names>Hilal</given-names>
                                </name>
                                                                    <aff>SAKARYA ÜNİVERSİTESİ, BİLGİSAYAR VE BİLİŞİM BİLİMLERİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-8711-6625</contrib-id>
                                                                <name>
                                    <surname>Balta</surname>
                                    <given-names>Musa</given-names>
                                </name>
                                                                    <aff>SAKARYA ÜNİVERSİTESİ</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20250930">
                    <day>09</day>
                    <month>30</month>
                    <year>2025</year>
                </pub-date>
                                        <volume>8</volume>
                                        <issue>3</issue>
                                        <fpage>518</fpage>
                                        <lpage>535</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20250217">
                        <day>02</day>
                        <month>17</month>
                        <year>2025</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20250917">
                        <day>09</day>
                        <month>17</month>
                        <year>2025</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2018, Sakarya University Journal of Computer and Information Sciences</copyright-statement>
                    <copyright-year>2018</copyright-year>
                    <copyright-holder>Sakarya University Journal of Computer and Information Sciences</copyright-holder>
                </permissions>
            
                                                                                                                        <abstract><p>The problems of traditional electricity grids have led to the emergence of smart grids. Unlike traditional energy systems, smart grids play an important role in the energy sector with their flexibility, programmability and reliability. However, the heterogeneous structure of smart grids consisting of different devices and protocols poses some problems in terms of complexity, service quality and security. In the literature, SDN (Software Defined Networks) paradigm is proposed as a solution to these problems. SDN and smart grid integration makes the energy sector more efficient, reliable and sustainable. On the other hand, smart meters used in the consumption area of smart grids provide instantaneous transmission of energy production and consumption data in homes to the center. With the support of IoT (Internet of Things) of these meters and components in the home area network (oven, IP camera, TV, etc.), the energy supply and demand balance can be managed more smoothly.In this study, a software-defined and IoT-based smart home architecture is proposed to obtain real energy consumption data. The proposed architecture is developed and implemented on the Mininet simulator with python code. As a result of simulations run under different process and attack scenarios, energy consumption data sets were created. A comparison of the anomaly detection performances of machine learning algorithms on the data sets that are considered to contribute to the literature has been made. As a result of this comparison, it was observed that the success rate of the random forest algorithm was higher than the other algorithms with 90-95 percent.</p></abstract>
                                                            
            
                                                                                        <kwd-group>
                                                    <kwd>SDN</kwd>
                                                    <kwd>  Smart grid</kwd>
                                                    <kwd>  IoT network</kwd>
                                                    <kwd>  Smart home</kwd>
                                                    <kwd>  Energy consumption</kwd>
                                            </kwd-group>
                            
                                                                                                                                                <funding-group specific-use="FundRef">
                    <award-group>
                                                    <funding-source>
                                <named-content content-type="funder_name">Sakarya University, Scientific Research Projects Unit</named-content>
                            </funding-source>
                                                                            <award-id>2022-6-23-68</award-id>
                                            </award-group>
                </funding-group>
                                </article-meta>
    </front>
    <back>
                            <ref-list>
                                    <ref id="ref1">
                        <label>1</label>
                        <mixed-citation publication-type="journal">Özçelik, İbrahim, et al. Center energy: A secure testbed infrastructure proposal for electricity power grid. In: 2021 International Conference on Information Security and Cryptology (ISCTURKEY). IEEE, 2021. p. 149-154.</mixed-citation>
                    </ref>
                                    <ref id="ref2">
                        <label>2</label>
                        <mixed-citation publication-type="journal">Rehmani, Mubashir Husain, et al. Software defined networks-based smart grid communication: A comprehensive survey. IEEE Communications Surveys\&amp; Tutorials, 2019, 21.3: 2637-2670.</mixed-citation>
                    </ref>
                                    <ref id="ref3">
                        <label>3</label>
                        <mixed-citation publication-type="journal">Demirci, Sedef; SAGIROGLU, Seref. Software-defined networking for improving security in smart grid systems. In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, 2018. p. 1021-1026.</mixed-citation>
                    </ref>
                                    <ref id="ref4">
                        <label>4</label>
                        <mixed-citation publication-type="journal">Soares, Arthur AZ, et al. 3AS: Authentication, authorization, and accountability for sdn-based smart grids. IEEE Access, 2021, 9: 88621-88640.</mixed-citation>
                    </ref>
                                    <ref id="ref5">
                        <label>5</label>
                        <mixed-citation publication-type="journal">Jung, Oliver, et al. Anomaly Detection in Smart Grids based on Software Defined Networks. In: SMARTGREENS. 2019. p. 157-164.</mixed-citation>
                    </ref>
                                    <ref id="ref6">
                        <label>6</label>
                        <mixed-citation publication-type="journal">Dileep, G. J. R. E. A survey on smart grid technologies and applications. Renewable energy, 2020, 146: 2589-2625.</mixed-citation>
                    </ref>
                                    <ref id="ref7">
                        <label>7</label>
                        <mixed-citation publication-type="journal">Al-Fuqaha, Ala, et al. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys \&amp; tutorials, 2015, 17.4: 2347-2376.</mixed-citation>
                    </ref>
                                    <ref id="ref8">
                        <label>8</label>
                        <mixed-citation publication-type="journal">Roman, Rodrigo; NAJERA, Pablo; LOPEZ, Javier. Securing the internet of things. Computer, 2011, 44.9: 51-58.</mixed-citation>
                    </ref>
                                    <ref id="ref9">
                        <label>9</label>
                        <mixed-citation publication-type="journal">Atzori, L., Iera, A., &amp; Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787-2805.</mixed-citation>
                    </ref>
                                    <ref id="ref10">
                        <label>10</label>
                        <mixed-citation publication-type="journal">Wang, Minxiao; YANG, Ning; WENG, Ning. Securing a Smart Home with a Transformer-Based IoT Intrusion Detection System. Electronics, 2023, 12.9: 2100.</mixed-citation>
                    </ref>
                                    <ref id="ref11">
                        <label>11</label>
                        <mixed-citation publication-type="journal">Alonazi, Wesam Abdulrhman, et al. SDN Architecture for Smart Homes Security with Machine Learning and Deep Learning. International Journal of Advanced Computer Science and Applications, 2022, 13.10.</mixed-citation>
                    </ref>
                                    <ref id="ref12">
                        <label>12</label>
                        <mixed-citation publication-type="journal">Chen, Jian, et al. A multi-layer security scheme for mitigating smart grid vulnerability against faults and cyber-attacks. Applied Sciences, 2021, 11.21: 9972.</mixed-citation>
                    </ref>
                                    <ref id="ref13">
                        <label>13</label>
                        <mixed-citation publication-type="journal">NIST (2018, 8 November). Update of the NIST Smart Grid Conceptual Model.</mixed-citation>
                    </ref>
                                    <ref id="ref14">
                        <label>14</label>
                        <mixed-citation publication-type="journal">Marikyan, Davit; PAPAGIANNIDIS, Savvas; ALAMANOS, Eleftherios. A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 2019, 138: 139-154.</mixed-citation>
                    </ref>
                                    <ref id="ref15">
                        <label>15</label>
                        <mixed-citation publication-type="journal">Zaidan, A. A.; ZAIDAN, B. B. A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations. Artificial Intelligence Review, 2020, 53.1: 141-165.</mixed-citation>
                    </ref>
                                    <ref id="ref16">
                        <label>16</label>
                        <mixed-citation publication-type="journal">Rondon, Luis Puche, et al. Survey on enterprise Internet-of-Things systems (E-IoT): A security perspective. Ad Hoc Networks, 2022, 125: 102728.</mixed-citation>
                    </ref>
                                    <ref id="ref17">
                        <label>17</label>
                        <mixed-citation publication-type="journal">Ravinder, M.; KULKARNI, Vikram. Intrusion detection in smart meters data using machine learning algorithms: A research report. Frontiers in Energy Research, 2023, 11: 1147431.</mixed-citation>
                    </ref>
                                    <ref id="ref18">
                        <label>18</label>
                        <mixed-citation publication-type="journal">Cao, Keyan, et al. An overview on edge computing research. IEEE access, 2020, 8: 85714-85728.</mixed-citation>
                    </ref>
                                    <ref id="ref19">
                        <label>19</label>
                        <mixed-citation publication-type="journal">Danbatta, Salim Jibrin; VAROL, Asaf. Comparison of Zigbee, Z-Wave, Wi-Fi, and bluetooth wireless technologies used in home automation. In: 2019 7th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2019. p. 1-5.</mixed-citation>
                    </ref>
                                    <ref id="ref20">
                        <label>20</label>
                        <mixed-citation publication-type="journal">Moustafa, Nour; SLAY, Jill. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 military communications and information systems conference (MilCIS). IEEE, 2015. p. 1-6.</mixed-citation>
                    </ref>
                                    <ref id="ref21">
                        <label>21</label>
                        <mixed-citation publication-type="journal">Ring, Markus, et al. Flow-based benchmark data sets for intrusion detection. In: Proceedings of the 16th European conference on cyber warfare and security. ACPI. 2017. p. 361-369.</mixed-citation>
                    </ref>
                                    <ref id="ref22">
                        <label>22</label>
                        <mixed-citation publication-type="journal">Sharafaldin, Iman; LASHKARI, Arash Habibi; GHORBANI, Ali A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 2018, 1: 108-116.</mixed-citation>
                    </ref>
                                    <ref id="ref23">
                        <label>23</label>
                        <mixed-citation publication-type="journal">MOUSTAFA, Nour. New generations of internet of things datasets for cybersecurity applications based machine learning: TON\_IoT datasets. In: Proceedings of the eResearch Australasia Conference, Brisbane, Australia. 2019. p. 21-25.</mixed-citation>
                    </ref>
                                    <ref id="ref24">
                        <label>24</label>
                        <mixed-citation publication-type="journal">NSL-KDD dataset. https://www.unb.ca/cic/datasets/nsl.html</mixed-citation>
                    </ref>
                                    <ref id="ref25">
                        <label>25</label>
                        <mixed-citation publication-type="journal">FAN, Cheng, et al. A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Frontiers in Energy Research, 2021, 9: 652801.</mixed-citation>
                    </ref>
                                    <ref id="ref26">
                        <label>26</label>
                        <mixed-citation publication-type="journal">YU, Xinran; ERGAN, Semiha; DEDEMEN, Gokmen. A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption. Applied Energy, 2019, 253: 113497.</mixed-citation>
                    </ref>
                                    <ref id="ref27">
                        <label>27</label>
                        <mixed-citation publication-type="journal">FAN, Cheng; XIAO, Fu; YAN, Chengchu. A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Automation in Construction, 2015, 50: 81-90.</mixed-citation>
                    </ref>
                                    <ref id="ref28">
                        <label>28</label>
                        <mixed-citation publication-type="journal">FAN, Cheng, et al. Temporal knowledge discovery in big BAS data for building energy management. Energy and Buildings, 2015, 109: 75-89.</mixed-citation>
                    </ref>
                                    <ref id="ref29">
                        <label>29</label>
                        <mixed-citation publication-type="journal">HASAN, Mahmudul, et al. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 2019, 7: 100059.</mixed-citation>
                    </ref>
                                    <ref id="ref30">
                        <label>30</label>
                        <mixed-citation publication-type="journal">GANGWAR, Amit Kumar; SHAIK, Abdul Gafoor. k-Nearest neighbour based approach for the protection of distribution network with renewable energy integration. Electric Power Systems Research, 2023, 220: 109301.</mixed-citation>
                    </ref>
                                    <ref id="ref31">
                        <label>31</label>
                        <mixed-citation publication-type="journal">ROSE, Thomas, et al. A hybrid anomaly-based intrusion detection system to improve time complexity in the Internet of Energy environment. Journal of Parallel and Distributed Computing, 2020, 145: 124-139.</mixed-citation>
                    </ref>
                                    <ref id="ref32">
                        <label>32</label>
                        <mixed-citation publication-type="journal">SHABAD, Prem Kumar Reddy; ALRASHIDE, Abdulmueen; MOHAMMED, Osama. Anomaly detection in smart grids using machine learning. In: IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2021. p. 1-8.</mixed-citation>
                    </ref>
                                    <ref id="ref33">
                        <label>33</label>
                        <mixed-citation publication-type="journal">LI, Qiang, et al. Simultaneous detection for multiple anomaly data in internet of energy based on random forest. Applied Soft Computing, 2023, 134: 109993.</mixed-citation>
                    </ref>
                                    <ref id="ref34">
                        <label>34</label>
                        <mixed-citation publication-type="journal">VIGOYA, Laura, et al. IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection. Electronics, 2021, 10.22: 2857.</mixed-citation>
                    </ref>
                                    <ref id="ref35">
                        <label>35</label>
                        <mixed-citation publication-type="journal">ARIBISALA, Adedayo; KHAN, Mohammad S.; HUSARI, Ghaith. Feed-Forward Intrusion Detection and Classification on a Smart Grid Network. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2022. p. 0099-0105.</mixed-citation>
                    </ref>
                                    <ref id="ref36">
                        <label>36</label>
                        <mixed-citation publication-type="journal">CHUANG, Po-Jen; LI, Si-Han. Network intrusion detection using hybrid machine learning. In: 2019 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2019. p. 1-5.</mixed-citation>
                    </ref>
                                    <ref id="ref37">
                        <label>37</label>
                        <mixed-citation publication-type="journal">SHI, Jibo, et al. A hybrid intrusion detection system based on machine learning under differential privacy protection. In: 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). IEEE, 2021. p. 1-6.</mixed-citation>
                    </ref>
                                    <ref id="ref38">
                        <label>38</label>
                        <mixed-citation publication-type="journal">Balta, D. D., Kaç, S. B., Balta, M., Oğur, N. B., &amp; Eken, S. (2025). Cybersecurity-aware log management system for critical water infrastructures. Applied Soft Computing, 169, 112613.</mixed-citation>
                    </ref>
                                    <ref id="ref39">
                        <label>39</label>
                        <mixed-citation publication-type="journal">Breviglieri, P., Erdem, T., &amp; Eken, S. (2021). Predicting smart grid stability with optimized deep models. SN Computer Science, 2, 1-12.</mixed-citation>
                    </ref>
                                    <ref id="ref40">
                        <label>40</label>
                        <mixed-citation publication-type="journal">Singh, C., &amp; Jain, A. K. (2024). A comprehensive survey on DDoS attacks detection &amp; mitigation in SDN-IoT network. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 100543.</mixed-citation>
                    </ref>
                                    <ref id="ref41">
                        <label>41</label>
                        <mixed-citation publication-type="journal">Chaganti, R., Suliman, W., Ravi, V., &amp; Dua, A. (2023). Deep learning approach for SDN-enabled intrusion detection system in IoT networks. Information, 14(1), 41.</mixed-citation>
                    </ref>
                            </ref-list>
                    </back>
    </article>
