Large-scale network intrusion detection based on distributed learning algorithm

2008 ◽  
Vol 8 (1) ◽  
pp. 25-35 ◽  
Author(s):  
Daxin Tian ◽  
Yanheng Liu ◽  
Yang Xiang
Keyword(s):  
Intrusion Detection ◽  
Large Scale ◽  
Learning Algorithm ◽  
Distributed Learning ◽  
Network Intrusion Detection ◽  
Large Scale Network ◽  
Network Intrusion ◽  
Scale Network

Mobile network intrusion detection for IoT system based on transfer learning algorithm

2018 ◽  
Vol 22 (S4) ◽  
pp. 9889-9904 ◽  
Author(s):  
Lianbing Deng ◽  
Daming Li ◽  
Xiang Yao ◽  
David Cox ◽  
Haoxiang Wang
Keyword(s):  
Intrusion Detection ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Mobile Network ◽  
Network Intrusion Detection ◽  
Network Intrusion

The Simulation and Analysis of the Large-Scale Intrusion Detection Model in Shuffle Networks

2014 ◽  
Vol 556-562 ◽  
pp. 2878-2881
Author(s):  
Ke Wei Li
Keyword(s):  
Intrusion Detection ◽  
Confidence Interval ◽  
Large Scale ◽  
Hypothesis Test ◽  
Network Intrusion Detection ◽  
Detection Rates ◽  
Feed Forward ◽  
High Loss ◽  
Network Intrusion ◽  
Feed Forward Network

There are some issues for the shuffle network intrusion detection, such as high loss detection rates and time-consuming procedures. This paper proposes a shuffle network intrusion detection method fusing the misuse behavior analysis and analyzes the network misuse behavior procedures. According to the damaged data flow balance features by network misuse behavior, the paper applies the hypothesis test in probability theory to evaluate whether the confidence interval excesses 0. If the confidence interval does not contain zero, it indicates the presence of feed-forward network intrusion; otherwise, there is no feed-forward network intrusion. The experimental results show that this method can effectively solve the multi-packet collaborative intrusion problems. Compared to traditional methods, the test speed and accuracy of the method is significantly improved.

Artificial Neural Network Based Intrusion Detection Method Combined with Manifold Learning Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 3170-3174
Author(s):  
Jin Guang Chen ◽  
Zhi Xiong Li
Keyword(s):  
Intrusion Detection ◽  
Manifold Learning ◽  
Detection Rate ◽  
Large Scale ◽  
Learning Algorithm ◽  
Feature Space ◽  
Locally Linear Embedding ◽  
Ann Model ◽  
Network Intrusion ◽  
Artificial Neural

Computer and network security is one of the most emergency issues for a large scale of applications. The unexpected intrusion may make terrible disaster to the network users. It is therefore imperative to detect the network attacks to prevent this kind of violations. The intrusion patter recognition is now a hot topic in this research area. The use of the artificial neural networks (ANN) can provide intelligent intrusion detection. However, the intrusion detection rate is often affected by the input feature vector of the ANN. This is because the original feature space always contains a certain number of useless features. To overcome this problem, a new network intrusion detection approach based on manifold learning nonlinear feature dimension descending and ANN classifier is presented in this paper. The locally linear embedding (LLE) algorithm was used to reduce the original intrusion feature space. Then the satisfactory ANN model with proper input features was obtained. The efficiency of the proposed method was evaluated with the real intrusion data. The analysis results show that the proposed approach has good intrusion detection rate, and performs better than the standard GA-ANN method.

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