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.

THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY

2004 ◽  
Vol 12 (01) ◽  
pp. 37-46 ◽  
Author(s):  
JIYE LIANG ◽  
ZHONGZHI SHI
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Computer Applications ◽  
Mathematical Tool

Rough set theory is a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty. In this paper, we introduce the concepts of information entropy, rough entropy and knowledge granulation in rough set theory, and establish the relationships among those concepts. These results will be very helpful for understanding the essence of concept approximation and establishing granular computing in rough set theory.

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.

Rough Set-hypergraph-based Feature Selection Approach for Intrusion Detection Systems

2016 ◽  
Vol 66 (6) ◽  
pp. 612 ◽  
Author(s):  
M.R. Gauthama Raman ◽  
K. Kannan ◽  
S.K. Pal ◽  
V. S. Shankar Sriram
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Rough Set ◽  
Network Architecture ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Detection Systems ◽  
Helly Property

Immense growth in network-based services had resulted in the upsurge of internet users, security threats and cyber-attacks. Intrusion detection systems (IDSs) have become an essential component of any network architecture, in order to secure an IT infrastructure from the malicious activities of the intruders. An efficient IDS should be able to detect, identify and track the malicious attempts made by the intruders. With many IDSs available in the literature, the most common challenge due to voluminous network traffic patterns is the curse of dimensionality. This scenario emphasizes the importance of feature selection algorithm, which can identify the relevant features and ignore the rest without any information loss. In this paper, a novel rough set κ-Helly property technique (RSKHT) feature selection algorithm had been proposed to identify the key features for network IDSs. Experiments carried using benchmark KDD cup 1999 dataset were found to be promising, when compared with the existing feature selection algorithms with respect to reduct size, classifier’s performance and time complexity. RSKHT was found to be computationally attractive and flexible for massive datasets.

A FPGA-Based Discretization Algorithm of Continuous Attributes in Rough Set

2013 ◽  
Vol 278-280 ◽  
pp. 1167-1173
Author(s):  
Guo Qiang Sun ◽  
Hong Li Wang ◽  
Jing Hui Lu ◽  
Xing He
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Processing Speed ◽  
Field Programmable Gate Array ◽  
Rough Set Theory ◽  
Continuous Data ◽  
Uncertain Information ◽  
Discretization Algorithm ◽  
Field Programmable ◽  
Speed Advantage

Rough set theory is mainly used for analysing, processing fuzzy and uncertain information and knowledge, but most of data that we usually gain are continuous data, rough set theory can pretreat these data and can gain satisfied discretization results. So, discretization of continuous attributes is an important part of rough set theory. Field Programmable Gate Array(FPGA) has been became the mainly platforms that realized design of digital system. In order to improve processing speed of discretization, this paper proposed a FPGA-based discretization algorithm of continuous attributes in rough ret that make use of the speed advantage of FPGA and combined attributes dependency degree. This method could save much time of pretreatment in rough ret and improve operation efficiency.

Review on Application of Rough Set Theory

2013 ◽  
Vol 694-697 ◽  
pp. 2905-2909
Author(s):  
Hua Yan
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Great Development

Rough set theory has found an increasingly wide utilization since it was promoted in 1980s.And study on the application of rough set theory in every field has a great development in recent years. Application of rough set theory in attribute reduction, continuous attributes discretization, and uncertainty measuring, as well as application of information entropy in rough set theory are reviewed in this paper. What we will do next is to probe further into the application of information entropy in rough set theory.

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