XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection

Faysal, Jabed Al; Mostafa, Sk Tahmid; Tamanna, Jannatul Sultana; Mumenin, Khondoker Mirazul; Arifin, Md. Mashrur; Awal, Md. Abdul; Shome, Atanu; Mostafa, Sheikh Shanawaz

http://hdl.handle.net/10400.13/5647

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Authors

Faysal, Jabed Al

Mostafa, Sk Tahmid

Tamanna, Jannatul Sultana

Mumenin, Khondoker Mirazul

Arifin, Md. Mashrur

Awal, Md. Abdul

Shome, Atanu

Mostafa, Sheikh Shanawaz

Abstract(s)

In the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT attacks. In this paper, a hybrid machine learning scheme called XGB-RF is proposed for detecting intrusion attacks. The proposed hybrid method was applied to the N-BaIoT dataset containing hazardous botnet attacks. Random forest (RF) was used for the feature selection and eXtreme Gradient Boosting (XGB) classifier was used to detect different types of attacks on IoT environments. The performance of the proposed XGB-RF scheme is evaluated based on several evaluation metrics and demonstrates that the model successfully detects 99.94% of the attacks. After comparing it with state-of-the-art algorithms, our proposed model has achieved better performance for every metric. As the proposed scheme is capable of detecting botnet attacks effectively, it can significantly contribute to reducing the security concerns associated with IoT systems.

Keywords

IoT security Botnet detection Random forest XGB Feature selection Mirai . Faculdade de Ciências Exatas e da Engenharia

URI

http://hdl.handle.net/10400.13/5647

Citation

Faysal, J.A.; Mostafa, S.T.; Tamanna, J.S.; Mumenin, K.M.; Arifin, M.M.; Awal, M.A.; Shome A.; Mostafa, S.S. XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection. Telecom 2022, 3, 52–69. https://doi.org/10.3390/ telecom3010003