Introducing a Classification Model Based on SVM for Network Intrusion Detection

2018 ◽  
pp. 54-66 ◽  
Author(s):  
Ghodratolah Dastfal ◽  
Samad Nejatian ◽  
Hamid Parvin ◽  
Vahideh Rezaie
Keyword(s):  
Intrusion Detection ◽  
Classification Model ◽  
Network Intrusion Detection ◽  
Model Based ◽  
Network Intrusion

Network Intrusion Detection Method Based on RS-LSSVM

2014 ◽  
Vol 602-605 ◽  
pp. 1634-1637
Author(s):  
Fang Nian Wang ◽  
Shen Shen Wang ◽  
Wan Fang Che ◽  
Yun Bai
Keyword(s):  
Intrusion Detection ◽  
Detection Method ◽  
Attribute Reduction ◽  
Feature Space ◽  
Least Square ◽  
Classification Model ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Network Intrusion ◽  
Sample Data

An intrusion detection method based on RS-LSSVM is studied in this paper. Firstly, attribute reduction algorithm based on the generalized decision table is proposed to remove the interference features and reduce the dimension of input feature space. Then the classification method based on least square support vector machine (LSSVM) is analyzed. The sample data after dimension reduction is used for LSSVM training, and the LSSVM classification model is obtained, which forms the ability of detecting unknown intrusion. Simulation results show that the proposed method can effectively remove the unnecessary features and improve the performance of network intrusion detection.

Network Intrusion Detection Model based on Chi-square Test and Stacking Approach

2020 ◽  
Author(s):  
Xiao Zheng ◽  
Yu Wang ◽  
Luliang Jia ◽  
Dapeng Xiong ◽  
Jie Qiang
Keyword(s):  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Chi Square ◽  
Detection Model ◽  
Model Based ◽  
Network Intrusion ◽  
Chi Square Test

scholarly journals Network Intrusion Detection Model Based on Artificial Intelligence

2020 ◽  
Vol 1617 ◽  
pp. 012082
Author(s):  
Qingchuan Meng ◽  
Youzi Zhang ◽  
Fengzhi Wu ◽  
Xiaoming Chen
Keyword(s):  
Artificial Intelligence ◽  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Detection Model ◽  
Model Based ◽  
Network Intrusion

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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