Predicting DDoS Attacks Using Machine Learning Algorithms in Building Management Systems

The 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 ap...

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Bibliographic Details
Published in:Electronics (Basel) 2023-10, Vol.12 (19), p.4142
Main Authors: Avcı, İsa, Koca, Murat
Format: Article
Language:English
Subjects:
Access control
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Building automation
Building management systems
Cybersecurity
Data mining
Data security
Denial of service attacks
Facility management
Facility management systems
Hazard mitigation
Internet of Things
K-nearest neighbors algorithm
Machine learning
Mathematical optimization
Methods
Neural networks
Optimization
Security systems
Sensors
Smart buildings
Software
Support vector machines
Technology application
Threat evaluation
Wide area networks
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Summary:The 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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12194142