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Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

Computers, Materials & Continua 2022年第9期72卷 5301-5314页

作者： Muhamad Erza Aminanto ibnu rifqi purbomukti Harry Chandra Kwangjo Kim School of Strategic and Global Studies Universitas IndonesiaDepok16424Indonesia National Institute of Information and Communications Technology(NICT) Koganei184-8795Japan Department of Electrical Engineering and Information Technology Universitas Gadjah MadaYogyakarta55281Indonesia School of Computing Korea Advanced Institute of Science and Technology(KAIST)Daejeon34141Korea

Internet of Things(IoT)networks leverage wireless communication protocols,which adversaries can *** attacks,injection attacks,and flooding are several examples of different attacks existing in Wi-Fi *** Detection System(IDS)became one solution to distinguish those attacks from benign *** learning techniques have been intensively utilized to classify the ***,the main issue of utilizing deep learning models is projecting the data,notably tabular data,into an *** study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural Network(CNN)models,*** define the particular sequence of placing the attribute values in a grid that would be captured as an *** the most important subset of attributes and EfficientNet,we aim for an accurate and lightweight IDS module deployed in IoT *** examine the proposed model using the Wi-Fi attacks dataset,called the AWID2 *** achieve the best performance by a 99.91%F1 score and 0.11%false-positive *** addition,our proposed model achieved comparable results with other statistical machine learning models,which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy.

关键词： Intrusion detection impersonation attack convolutional neural network anomaly detection

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