Predicting DDoS Attacks Using Machine Learning Algorithms in Building Management Systems

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dc.authoridAVCI, Dr. Isa/0000-0001-7032-8018
dc.authoridKoca, Murat/0000-0002-6048-7645
dc.contributor.authorAvci, Isa
dc.contributor.authorKoca, Murat
dc.date.accessioned2024-09-29T16:08:05Z
dc.date.available2024-09-29T16:08:05Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe rapid growth of the Internet of Things (IoT) in smart buildings necessitates the continuous evaluation of potential threats and their implications. Conventional methods are increasingly inadequate in measuring risk and mitigating associated hazards, necessitating the development of innovative approaches. Cybersecurity systems for IoT are critical not only in Building Management System (BMS) applications but also in various aspects of daily life. Distributed Denial of Service (DDoS) attacks targeting core BMS software, particularly those launched by botnets, pose significant risks to assets and safety. In this paper, we propose a novel algorithm that combines the power of the Slime Mould Optimization Algorithm (SMOA) for feature selection with an Artificial Neural Network (ANN) predictor and the Support Vector Machine (SVM) algorithm. Our enhanced algorithm achieves an outstanding accuracy of 97.44% in estimating DDoS attack risk factors in the context of BMS. Additionally, it showcases a remarkable 99.19% accuracy in predicting DDoS attacks, effectively preventing system disruptions, and managing cyber threats. To further validate our work, we perform a comparative analysis using the K-Nearest Neighbor Classifier (KNN), which yields an accuracy rate of 96.46%. Our model is trained on the Canadian Institute for Cybersecurity (CIC) IoT Dataset 2022, enabling behavioral analysis and vulnerability testing on diverse IoT devices utilizing various protocols, such as IEEE 802.11, Zigbee-based, and Z-Wave.en_US
dc.description.sponsorshipThis research utilized the IoT Dataset 2022 provided by the Canadian Institute for Cybersecurity (CIC). The researchers would like to express gratitude to Sajjad Dadkhah, Hassan Mahdikhani, Priscilla Kyei Danso, Alireza Zohourian, Kevin Anh Truong, and Ali; Canadian Institute for Cybersecurity (CIC)en_US
dc.description.sponsorshipThis research utilized the IoT Dataset 2022 provided by the Canadian Institute for Cybersecurity (CIC). The researchers would like to express gratitude to Sajjad Dadkhah, Hassan Mahdikhani, Priscilla Kyei Danso, Alireza Zohourian, Kevin Anh Truong, and Ali A. Ghorbani for their collaboration in profiling the realistic, multi-dimensional IoT dataset.en_US
dc.identifier.doi10.3390/electronics12194142
dc.identifier.issn2079-9292
dc.identifier.issue19en_US
dc.identifier.scopus2-s2.0-85173819624en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/electronics12194142
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7349
dc.identifier.volume12en_US
dc.identifier.wosWOS:001089122500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofElectronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcybersecurityen_US
dc.subjectdistributed denial of service attacksen_US
dc.subjectinternet of things (IoT)en_US
dc.subjectintrusion detection systemsen_US
dc.subjectslime mould optimization algorithmen_US
dc.titlePredicting DDoS Attacks Using Machine Learning Algorithms in Building Management Systemsen_US
dc.typeArticleen_US

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