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Abbasi, Fereshteh, Naderan, Marjan, Alavi, Seyyed Enayatallah. (1400). Intrusion Detection in IoT With Logistic Regression and Artificial Neural Network: Further Investigations on N-BaIoT Dataset Devices. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 8(2), 27-42. doi: 10.22108/jcs.2021.129807.1077
Fereshteh Abbasi; Marjan Naderan; Seyyed Enayatallah Alavi. "Intrusion Detection in IoT With Logistic Regression and Artificial Neural Network: Further Investigations on N-BaIoT Dataset Devices". سامانه مدیریت نشریات علمی دانشگاه اصفهان, 8, 2, 1400, 27-42. doi: 10.22108/jcs.2021.129807.1077
Abbasi, Fereshteh, Naderan, Marjan, Alavi, Seyyed Enayatallah. (1400). 'Intrusion Detection in IoT With Logistic Regression and Artificial Neural Network: Further Investigations on N-BaIoT Dataset Devices', سامانه مدیریت نشریات علمی دانشگاه اصفهان, 8(2), pp. 27-42. doi: 10.22108/jcs.2021.129807.1077
Abbasi, Fereshteh, Naderan, Marjan, Alavi, Seyyed Enayatallah. Intrusion Detection in IoT With Logistic Regression and Artificial Neural Network: Further Investigations on N-BaIoT Dataset Devices. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 1400; 8(2): 27-42. doi: 10.22108/jcs.2021.129807.1077

Intrusion Detection in IoT With Logistic Regression and Artificial Neural Network: Further Investigations on N-BaIoT Dataset Devices

Journal of Computing and Security
مقاله 4، دوره 8، شماره 2، مهر 2021، صفحه 27-42    اصل مقاله (1.14 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2021.129807.1077
نویسندگان
Fereshteh Abbasi؛ Marjan Naderan* ؛ Seyyed Enayatallah Alavi
Department of Computer Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
چکیده
Due to the increasing development and applications of the Internet of Things (IoT), detection and prevention of intruders into the network and devices has gained much attention in the past decade. For this challenge, traditional solutions of Intrusion Detection Systems (IDS) are not responsive in IoT environments or at least may not be very efficient. In this article, we deeply investigate the previous methods of using machine learning methods for intrusion detection in IoT, and two methods for feature extraction and classification are proposed. The first method is feature extraction and classification using Logistic Regression (LR) and the second method is to use an Artificial Neural Network (ANN) for classification. To evaluate the performance of the proposed method, six devices of the N_BaIoT dataset, which consists of data samples related to nine devices IoT and several attacks are used according to some criteria for evaluating the performance of the proposed methods. Simulation results in comparison with some other deep learning methods in terms of accuracy, precision, recall and F1-score show that using logistic regression, is more efficient and above 90% classification accuracy is achieved.
کلیدواژه‌ها
Internet of Thing؛ Anomaly Detection؛ Artificial Neural Network؛ Logistic Regression؛ Botnet
سایر فایل های مرتبط با مقاله
مراجع
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