Research Article
Research on Random Intrusion Depth Detection of Internet of Things Based on 3D Convolutional Neural Network
@INPROCEEDINGS{10.1007/978-3-031-50571-3_19, author={Xingfei Ma and Wuguang Wang}, title={Research on Random Intrusion Depth Detection of Internet of Things Based on 3D Convolutional Neural Network}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2024}, month={2}, keywords={Three Dimensional Convolution Neural Network Internet of Things Random Intrusion Depth Detection Method}, doi={10.1007/978-3-031-50571-3_19} }- Xingfei Ma
Wuguang Wang
Year: 2024
Research on Random Intrusion Depth Detection of Internet of Things Based on 3D Convolutional Neural Network
ICMTEL
Springer
DOI: 10.1007/978-3-031-50571-3_19
Abstract
There are many problems in the industrial Internet of Things, such as low feature extraction rate, low detection efficiency and poor adaptability. To solve this problem, a random intrusion depth detection method based on three-dimensional convolution neural network is proposed. According to NIDS, an intrusion detection model of the Internet of Things is built, through which distributed network data packets are collected, and the principal component analysis algorithm is used to preprocess them to reduce data dimensions. Combined with deep learning theory and technology, select data features to form feature matrix. With this as the input, the random intrusion detection in the Internet of Things is completed by using 3D convolution neural network (3DCNN) combined with long and short memory (LSTM) method. The experimental results show that the F1 value of the detection method is above 0.9, indicating that the detection accuracy of the method is high.
