Machine learning based models for detecting attacks in IoT systems

Olaya, Maria Katherine Plazas; Martínez, David Santiago Guerrero; Tejada, Jaime Alberto Vergara; Cobo, José Edinson Aedo

doi:10.1109/iceccme57830.2023.10252382

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2024

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Cited by 2 publications

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Machine Learning Algorithms for Binary and Multiclass Classification of IoT Network Traffic in Smart Cities

Kumar,

Sunehra

2024

2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT)

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Machine Learning Algorithms for Binary and Multiclass Classification of IoT Network Traffic in Smart Cities

Kumar,

Sunehra

2024

2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT)

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Securing Microservices‐Based IoT Networks: Real‐Time Anomaly Detection Using Machine Learning

Plazas Olaya,

Vergara Tejada,

Aedo Cobo

2024

Journal of Computer Networks and Communications

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Increased attention is being given to Internet of things (IoT) network security due to attempts to exploit vulnerabilities. Security techniques protecting availability, confidentiality, and information integrity have intensified as IoT devices are viewed as gateways to larger networks by malicious actors. As an additional factor, the microservices‐based platforms have overtaken the deployment of applications that support smart cities; however, the distributed nature of these architectures heightens susceptibility to malicious network infrastructure use. These risks can result in disruptions to system functioning or data compromise. Proposed strategies to mitigate these risks include developing intrusion detection systems and utilizing machine learning to differentiate between normal and anomalous network traffic, indicating potential attacks. This article outlines the development and implementation of an intrusion detection system (IDS) using machine learning to detect online anomalies in network traffic. Comprising a traffic extractor and anomaly detector, the system employs supervised learning with various datasets to train models. The results demonstrate the effectiveness of the decision tree model in detecting traditional denial of service (DoS) attacks, achieving high scores across multiple metrics: an F1‐score of 98.08%, precision of 99.25%, recall of 96.96%, and accuracy of 99.62%. The random forest model excels in identifying slow‐rate DoS attacks, attaining an F1‐score of 99.85%, precision of 99.91%, recall of 99.80%, and accuracy of 99.88%.

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Machine learning based models for detecting attacks in IoT systems

Cited by 2 publications

References 22 publications

Machine Learning Algorithms for Binary and Multiclass Classification of IoT Network Traffic in Smart Cities

Machine Learning Algorithms for Binary and Multiclass Classification of IoT Network Traffic in Smart Cities

Securing Microservices‐Based IoT Networks: Real‐Time Anomaly Detection Using Machine Learning

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