scholarly journals Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ming Li ◽  
Dezhi Han ◽  
Xinming Yin ◽  
Han Liu ◽  
Dun Li
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Network Traffic ◽  
Rapid Development ◽  
Widespread Application ◽  
Detection Model ◽  
Spatial Features ◽  
Network Traffic Classification ◽  
Temporal And Spatial ◽  
Traffic Detection

With the rapid development and widespread application of cloud computing, cloud computing open networks and service sharing scenarios have become more complex and changeable, causing security challenges to become more severe. As an effective means of network protection, anomaly network traffic detection can detect various known attacks. However, there are also some shortcomings. Deep learning brings a new opportunity for the further development of anomaly network traffic detection. So far, the existing deep learning models cannot fully learn the temporal and spatial features of network traffic and their classification accuracy needs to be improved. To fill this gap, this paper proposes an anomaly network traffic detection model integrating temporal and spatial features (ITSN) using a three-layer parallel network structure. ITSN learns the temporal and spatial features of the traffic and fully fuses these two features through feature fusion technology to improve the accuracy of network traffic classification. On this basis, an improved method of raw traffic feature extraction is proposed, which can reduce redundant features, speed up the convergence of the network, and ease the imbalance of the datasets. The experimental results on the ISCX-IDS 2012 and CICIDS 2017 datasets show that the ITSN can improve the accuracy of anomaly network traffic detection while enhancing the robustness of the detection system and has a higher recognition rate for positive samples.

scholarly journals Deep Learning-Based Encrypted Network Traffic Classification and Resource Allocation in SDN

2021 ◽  
Author(s):  
Hao Wu ◽  
Xi Zhang ◽  
Jufeng Yang
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Network Traffic ◽  
Rapid Development ◽  
Research Direction ◽  
Traffic Classification ◽  
Network Technology ◽  
Main Research ◽  
Network Information ◽  
Network Traffic Classification

In the rapid development of network technology, with the improvement of the quality and quantity of network users’ demands, more and more network information technology and excessive network traffic also raise people’s attention to the internal network security. Especially for the classification and resource allocation of encrypted network traffic, the research of related technologies has become the main research direction of the development of network technology. The extensive application of deep learning provides a new idea for the study of traffic classification. Therefore, on the basis of understanding the current situation, the improved convolutional neural network is selected to conduct an in-depth discussion on traffic classification and resource allocation of encrypted networks based on deep learning. The performance of the system is verified from the perspective of practical application.

scholarly journals DeepHTTP: Anomalous HTTP Traffic Detection and Malicious Pattern Mining Based on Deep Learning

2020 ◽  
pp. 141-161
Author(s):  
Yuqi Yu ◽  
Hanbing Yan ◽  
Yuan Ma ◽  
Hao Zhou ◽  
Hongchao Guan
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Pattern Mining ◽  
Short Term Memory ◽  
Attack Detection ◽  
Attention Mechanism ◽  
Discriminative Ability ◽  
Feature Extraction Method ◽  
Detection Model ◽  
Traffic Detection

AbstractHypertext Transfer Protocol (HTTP) accounts for a large portion of Internet application-layer traffic. Since the payload of HTTP traffic can record website status and user request information, many studies use HTTP protocol traffic for web application attack detection. In this work, we propose DeepHTTP, an HTTP traffic detection framework based on deep learning. Unlike previous studies, this framework not only performs malicious traffic detection but also uses the deep learning model to mine malicious fields of the traffic payload. The detection model is called AT-Bi-LSTM, which is based on Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism. The attention mechanism can improve the discriminative ability and make the result interpretable. To enhance the generalization ability of the model, this paper proposes a novel feature extraction method. Experiments show that DeepHTTP has an excellent performance in malicious traffic discrimination and pattern mining.

scholarly journals Network Intrusion Detection Based on an Efficient Neural Architecture Search

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1453
Author(s):  
Renjian Lyu ◽  
Mingshu He ◽  
Yu Zhang ◽  
Lei Jin ◽  
Xinlei Wang
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Traffic Monitoring ◽  
Classification Model ◽  
Network Intrusion Detection ◽  
Traffic Classification ◽  
Neural Architecture ◽  
Network Intrusion ◽  
Network Traffic Classification

Deep learning has been applied in the field of network intrusion detection and has yielded good results. In malicious network traffic classification tasks, many studies have achieved good performance with respect to the accuracy and recall rate of classification through self-designed models. In deep learning, the design of the model architecture greatly influences the results. However, the design of the network model architecture usually requires substantial professional knowledge. At present, the focus of research in the field of traffic monitoring is often directed elsewhere. Therefore, in the classification task of the network intrusion detection field, there is much room for improvement in the design and optimization of the model architecture. A neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal. For this reason, we propose a model that can perform NAS in the field of network traffic classification and search for the optimal architecture suitable for traffic detection based on the network traffic dataset. Each layer of our depth model is constructed according to the principle of maximum coding rate attenuation, which has strong consistency and symmetry in structure. Compared with some manually designed network architectures, classification indicators, such as Top-1 accuracy and F1 score, are also greatly improved while ensuring the lightweight nature of the model. In addition, we introduce a surrogate model in the search task. Compared to using the traditional NAS model to search the network traffic classification model, our NAS model greatly improves the search efficiency under the premise of ensuring that the results are not substantially different. We also manually adjust some operations in the search space of the architecture search to find a set of model operations that are more suitable for traffic classification. Finally, we apply the searched model to other traffic datasets to verify the universality of the model. Compared with several common network models in the traffic field, the searched model (NAS-Net) performs better, and the classification effect is more accurate.

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