A new framework for intrusion detection based on rough set theory

2004 ◽  
Author(s):  
Zhijun Li ◽  
Yu Wu ◽  
Guoyin Wang ◽  
Yongjun Hai ◽  
Yunpeng He
Keyword(s):  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
New Framework

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.

The Discretization Algorithm Based on Rough Set and its Application

2013 ◽  
Vol 416-417 ◽  
pp. 1399-1403 ◽  
Author(s):  
Zhi Cai Shi ◽  
Yong Xiang Xia ◽  
Chao Gang Yu ◽  
Jin Zu Zhou
Keyword(s):  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Rough Set Theory ◽  
Information Loss ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Discretization Algorithm ◽  
Response Performance

The discretization is one of the most important steps for the application of Rough set theory. In this paper, we analyzed the shortcomings of the current relative works. Then we proposed a novel discretization algorithm based on information loss and gave its mathematical description. This algorithm used information loss as the measure so as to reduce the loss of the information entropy during discretizating. The algorithm was applied to different samples with the same attributes from KDDcup99 and intrusion detection systems. The experimental results show that this algorithm is sensitive to the samples only for parts of all attributes. But it dose not compromise the effect of intrusion detection and it improves the response performance of intrusion detection remarkably.

Rough Sets Based Rule Generation from Data with Categorical and Numerical Values

2008 ◽  
Vol 12 (5) ◽  
pp. 426-434 ◽  
Author(s):  
Hiroshi Sakai ◽  
◽  
Kazuhiro Koba ◽  
Michinori Nakata ◽  
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Rule Generation ◽  
Utility Program ◽  
New Framework

Rough set theory has been mainly applied to data with categorical values. In order to handle data with numerical values in this theory, a familiar concept of ‘wildcards’ was employed, and a new framework of rough sets based rule generation has been proposed. Two characters @ and # were introduced into this framework, and numerical patterns were also defined for numerical values. The concepts of ‘coarse’ and ‘fine’ for rules were explicitly defined according to numerical patterns. This paper enhances the previous framework, and describes the implementation of an utility program. This utility program is applied to the data in UCI Machine Learning Repository, and some useful rules are obtained.

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