A Network Intrusion Detection Method Based on Deep Multi-scale Convolutional Neural Network

Wang, Xiaowei; Yin, Shoulin; Li, Hang; Wang, Jiachi; Teng, Lin

doi:10.1007/s10776-020-00495-3

Cited by 52 publications

(26 citation statements)

References 33 publications

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“…As shown in Figure 1 , the 3-D multiscale dense residual network is divided into three parts: the shallow feature extraction layer, the multiscale dense residual layer, and the global feature aggregation layer (Wang et al, 2020b ). The shallow feature extraction layer (Part A) includes the two 3-D Conv, and the multiscale residual dense layer (Part B) includes a pooling layer (Maxpool), multiple residual dense blocks (3D-RDB), and convolutional layer 3D Conv1 and 3D Conv2 for convolution down-sampling.…”

Section: Proposed Behavior Recognition Frameworkmentioning

confidence: 99%

Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

Liu

Zhang

et al. 2021

Front. Neurorobot.

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The study of student behavior analysis in class plays a key role in teaching and educational reforms that can help the university to find an effective way to improve students' learning efficiency and innovation ability. It is also one of the effective ways to cultivate innovative talents. The traditional behavior recognition methods have many disadvantages, such as poor robustness and low efficiency. From a heterogeneous view perception point of view, it introduces the students' behavior recognition. Therefore, we propose a 3-D multiscale residual dense network from heterogeneous view perception for analysis of student behavior recognition in class. First, the proposed method adopts 3-D multiscale residual dense blocks as the basic module of the network, and the module extracts the hierarchical features of students' behavior through the densely connected convolutional layer. Second, the local dense feature of student behavior is to learn adaptively. Third, the residual connection module is used to improve the training efficiency. Finally, experimental results show that the proposed algorithm has good robustness and transfer learning ability compared with the state-of-the-art behavior recognition algorithms, and it can effectively handle multiple video behavior recognition tasks. The design of an intelligent human behavior recognition algorithm has great practical significance to analyze the learning and teaching of students in the class.

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Section: Proposed Behavior Recognition Frameworkmentioning

confidence: 99%

Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

Liu

Zhang

et al. 2021

Front. Neurorobot.

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“…The spatio-temporal attention module is composed of CNN-based temporal attention model [19], multi-spatial attention model and the fusion of spatio-temporal features. The temporal attention model and the multispatial attention model focus on key frames and multiple saliency behaviour regions from the temporal and spatial dimensions of the video, respectively.…”

Section: Spatio-temporal Attention Modulementioning

confidence: 99%

A spatio-temporal attention fusion model for students behaviour recognition

Wang¹

2018

ICST Transactions on Scalable Information Systems

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Student behavior analysis can reflect students' learning situation in real time, which provides an important basis for optimizing classroom teaching strategies and improving teaching methods. It is an important task for smart classroom to explore how to use big data to detect and recognize students behavior. Traditional recognition methods have some defects, such as low efficiency, edge blur, time-consuming, etc. In this paper, we propose a new students behaviour recognition method based on spatio-temporal attention fusion model. It makes full use of key spatio-temporal information of video, the problem of spatio-temporal information redundancy is solved. Firstly, the channel attention mechanism is introduced into the spatio-temporal network, and the channel information is calibrated by modeling the dependency relationship between feature channels. It can improve the expression ability of features. Secondly, a time attention model based on convolutional neural network (CNN) is proposed, which uses fewer parameters to learn the attention score of each frame, focusing on the frames with obvious behaviour amplitude. Meanwhile, a multi-spatial attention model is presented to calculate the attention score of each position in each frame from different angles, extract several saliency areas of behaviour, and fuse the spatio-temporal features to further enhance the feature representation of video. Finally, the fused features are input into the classification network, and the behaviour recognition results are obtained by combining the two output streams according to different weights. Experiment results on HMDB51, UCF101 datasets and eight typical classroom behaviors of students show that the proposed method can effectively recognize the behaviours in videos. The accuracy of HMDB51 is higher than 90%, that of UCF101 and real data are higher than 90%.

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“…The current research is mainly based on deep learning methods. The object detection network based on deep learning consists of two basic parts: feature extraction module and object detection module [13,14]. When convolutional neural network (CNN) is used to extract image features, the deep feature graph has rich object semantic information and it is sensitive to category information.…”

Section: Related Workmentioning

confidence: 99%

Multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection

Li¹

2018

ICST Transactions on Scalable Information Systems

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In complex background, the accuracy of moving object detection can be affected by some factors such as illumination change, short occlusion and background movement. This paper proposes a new multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection. The proposed model uses a variety of ways to fuse the feature information. First, the multi-layer feature map information with different sizes is fused by the per-pixel addition method. Then the feature map of different stages is splicing in the channel dimension to form the information fusion feature layer with rich semantic information and detail information as the prediction layer of the model. In this model, convolution kernel pyramid structure is introduced into the anchor frame mechanism to solve the multi-scale problem of detecting objects. The number of parameters increased by large convolution kernel is reduced by using dilated convolution to reduce the number of anchor frames reasonably. Experimental results show that the proposed fusion algorithm has certain anti-interference ability and high precision for moving object detection in complex environment compared the state-of-the-art methods.

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A Network Intrusion Detection Method Based on Deep Multi-scale Convolutional Neural Network

Cited by 52 publications

References 33 publications

Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

A spatio-temporal attention fusion model for students behaviour recognition

Multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection

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