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Internet of Things Intrusion Detection System Based on Convolutional Neural Network

查看全文 作　　者：Jie [1,2,3]Yin;Yuxuan [1]Shi;Wen [1]Deng;Chang [1]Yin;Tiannan [1]Wang;Yuchen [1]Song;Tianyao [1]Li;Yicheng [1]Li 高影响力作者 机构地区：[1]Department of Computer Information and Cyber Security,Jiangsu Police Institute,Nanjing,China;[2]Engineering Research Center of Electronic Data Forensics Analysis,Nanjing,China;[3]Key Laboratory of Digital Forensics,Department of Public Security of Jiangsu Province,Nanjing,China高影响力机构 出　　处：《Computers, Materials & Continua》索引2023年第4期,共17页高影响力期刊 摘　　要：In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output layer.Finally, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time. 关 键 词：Internet of things intrusion detection system convolutional neural network federated learning
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