scholarly journals Decomposing Training Data to Improve Network Intrusion Detection Performance

2021 ◽  
Author(s):  
Roberto Saia ◽  
Alessandro Podda ◽  
Gianni Fenu ◽  
Riccardo Balia
Keyword(s):  
Intrusion Detection ◽  
Detection Performance ◽  
Training Data ◽  
Network Intrusion Detection ◽  
Network Intrusion

scholarly journals Deep Stacking Network for Intrusion Detection

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 25
Author(s):  
Yifan Tang ◽  
Lize Gu ◽  
Leiting Wang
Keyword(s):  
Intrusion Detection ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Performance ◽  
Network Intrusion Detection ◽  
Fusion Model ◽  
Data Set ◽  
Network Intrusion

Preventing network intrusion is the essential requirement of network security. In recent years, people have conducted a lot of research on network intrusion detection systems. However, with the increasing number of advanced threat attacks, traditional intrusion detection mechanisms have defects and it is still indispensable to design a powerful intrusion detection system. This paper researches the NSL-KDD data set and analyzes the latest developments and existing problems in the field of intrusion detection technology. For unbalanced distribution and feature redundancy of the data set used for training, some training samples are under-sampling and feature selection processing. To improve the detection effect, a Deep Stacking Network model is proposed, which combines the classification results of multiple basic classifiers to improve the classification accuracy. In the experiment, we screened and compared the performance of various mainstream classifiers and found that the four models of the decision tree, k-nearest neighbors, deep neural network and random forests have outstanding detection performance and meet the needs of different classification effects. Among them, the classification accuracy of the decision tree reaches 86.1%. The classification effect of the Deeping Stacking Network, a fusion model composed of four classifiers, has been further improved and the accuracy reaches 86.8%. Compared with the intrusion detection system of other research papers, the proposed model effectively improves the detection performance and has made significant improvements in network intrusion detection.

scholarly journals A Few-Shot Learning-Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zu-Min Wang ◽  
Ji-Yu Tian ◽  
Jing Qin ◽  
Hui Fang ◽  
Li-Ming Chen
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Metric Learning ◽  
Training Data ◽  
Superior Performance ◽  
Sampling Scheme ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Imbalanced Training Data ◽  
Dynamic Relationships

Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine-learning-based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few-shot learning-based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well-designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state-of-the-art methods, our proposed method achieves superior performance to effectively detect both types of attacks.

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