scholarly journals An Intrusion Detection Algorithm based on D-S theory and Rough Set

2013 ◽  
Vol 9 (S6) ◽  
pp. 19
Author(s):  
Lifang Wang ◽  
Shuhai Zhang
Keyword(s):  
Intrusion Detection ◽  
Rough Set ◽  
Detection Algorithm

Intrusion Detection on Hybrid Gene Expression Programming

2011 ◽  
Vol 143-144 ◽  
pp. 526-530 ◽  
Author(s):  
S. Deng ◽  
W.M. Lin ◽  
T. Zhang
Keyword(s):  
Gene Expression ◽  
Intrusion Detection ◽  
Rough Set ◽  
High Dimension ◽  
Detection Efficiency ◽  
Gene Expression Programming ◽  
Detection Algorithm ◽  
Data Sets ◽  
Hybrid Gene ◽  
High Dimension Data

This paper present intrusion detection on hybrid gene expression programming (ID-HGEP), which combined theory of attribution reduction in rough set to improve precision and efficiency on intrusion detection. Through extensive experiments on high dimension data sets, it is shown that ID-HGEP is apparently more advantageous in terms of convergence speed, intrusion detection efficiency and average consumptive time in contrast with traditional intrusion detection algorithm on GEP.

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

NB Tree Based Intrusion Detection Technique Using Rough Set Theory Model

2019 ◽  
pp. 93-101
Author(s):  
Neha Gupta ◽  
Ritu Prasad ◽  
Praneet Saurabh ◽  
Bhupendra Verma
Keyword(s):  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Theory Model ◽  
Detection Technique

Intrusion Detection Using Modern Techniques

2011 ◽  
pp. 259-273
Author(s):  
Tarum Bhaskar ◽  
Narasimha Kamath B.
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Intrusion Detection System ◽  
Rough Set Theory ◽  
Detection System ◽  
Mining Tools

Intrusion detection system (IDS) is now becoming an integral part of the network security infrastructure. Data mining tools are widely used for developing an IDS. However, this requires an ability to find the mapping from the input space to the output space with the help of available data. Rough sets and neural networks are the best known data mining tools to analyze data and help solve this problem. This chapter proposes a novel hybrid method to integrate rough set theory, genetic algorithm (GA), and artificial neural network. Our method consists of two stages: First, rough set theory is applied to find the reduced dataset. Second, the results are used as inputs for the neural network, where a GA-based learning approach is used to train the intrusion detection system. The method is characterized not only by using attribute reduction as a pre-processing technique of an artificial neural network but also by an improved learning algorithm. The effectiveness of the proposed method is demonstrated on the KDD cup data.

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