عنوان مقاله:

نویسندگان:

فرشته عباسی، مرجان نادران طحان، سید عنایت اله علوی

With the paradigm of the Internet of Things (IoT) physical devices are connected to each other and to the Internet such that they operate automatically. One of the major challenges in the IoT is to detect and prevent intruders into the network and devices a challenge that traditional solutions of Intrusion Detection Systems (IDS) are not responsive or at least not very efficient to use in IoT. In this article we address the problem of using machine learning methods for anomaly detection 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). To evaluate the performance of the proposed method the N_BaIoT database which is dedicated to the IoT scenarios and consists of date from nine devices is used and several criteria for evaluating the performance of the proposed methods are used. Simulation results in comparison with other three deep learning methods in terms of F1-score show that using logistic regression is more .efficient and the highest classification accuracy (equivalent to 99.98%) can be achieved

Internet of Thing anomaly detection Artificial Neural Network Logistic Regression Botnet

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عباسی، فرشته، نادران طحان، مرجان، عنایت اله علوی، سید، 1400، تشخیص ناهنجاری در اینترنت اشیا با استفاده از انتخاب ویژگی و طبقه بندی با استفاده از رگرسیون لجستیک و شبکه عصبی مصنوعی بر روی مجموعه داده N-BaIoT، پنجمين دوره كنفرانس بین‌المللی اينترنت اشيا و كاربردها، https://conf.ui.ac.ir/fa/archive_index.php?c=iot2021&aid=1506490