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Title	Hybrid Model For Iot-Enabled Intelligent Towns Using The Mqtt-Iot-Ids2020 Dataset
ID_Doc	29780
Authors	Zupash; Rakha M.A.; Khan I.U.; Ouaissa M.; Ouaissa M.; Ayub M.Y.
Year	2024
Published	Cyber Security for Next-Generation Computing Technologies
DOI	http://dx.doi.org/10.1201/9781003404361-9
Abstract	This book chapter presents a hybrid model to identify attacks on IoT-enabled intelligent towns. As people are moving toward cities, security is becoming a major problem. Therefore, a smart HIDS (hybrid intrusion detection system) is needed to safeguard the overall system. Cyber attacks are quite dangerous as they can disrupt all communication within smart cities. Data traffic needs to be properly observed to check each data packet, so that normal/abnormal information can be easily extracted. Intruders try to gain control of the IoT network by sending malicious data packets. However, a hybrid intrusion detection system combines signature and anomaly detection to detect cyber attacks. More interestingly, the MQTT-IoT-IDS2020 dataset is used to evaluate the performance of logistic regression, K-NN, Gaussian naï ve Bayes, decision tree, Adaboost, random forest, and the hybrid model. In the simulation results, the hybrid model has optimal results in comparison with other techniques. Furthermore, this chapter briefly explains IoT design/architecture, cyber attacks, anomalies, hybrid IDS, and machine learning-enabled IDS, and a comparative study is incorporated. © 2024 selection and editorial matter, Inam Ullah Khan, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Abou El Houda and Muhammad Fazal Ijaz; individual chapters, the contributors.
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