A Case Study of Distributed Network Fault Detection Technology in Distance Education

2013 ◽  
Vol 760-762 ◽  
pp. 1282-1287
Author(s):  
Qian Jun Tang ◽  
Yan Zhang ◽  
Yong Ju Li
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
High Efficiency ◽  
Detection System ◽  
Attribute Reduction ◽  
Security Technology ◽  
Whole Process ◽  
Detection Technology ◽  
Network Fault

The intrusion detection under the environment of IPv6 is an important security technology along with firewall in system security defense system, which can be used for real-time detection and monitoring of the system in the whole process of system invasion. This paper puts forward an intrusion detection system under IPv6 platform based on intrusion detection feature attribute reduction by using pattern matching, so as to expand the range of application and user group of the security products. By the analysis and comparison of various pattern matching algorithms, the new algorithm realizes the intrusion feature module matching under IPv6, and make detection system be of high efficiency. Later experiments have proved this view.

An Intrusion Detection Method Study under the Environment of IPv6

2013 ◽  
Vol 760-762 ◽  
pp. 2238-2243
Author(s):  
Chao Wang ◽  
Wang Bin ◽  
Zong Li Zhang
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
Intrusion Detection System ◽  
High Efficiency ◽  
Detection Method ◽  
Detection System ◽  
Attribute Reduction ◽  
Security Technology ◽  
Whole Process ◽  
Feature Attribute

The intrusion detection under the environment of IPv6 is an important security technology along with firewall in system security defense system, which can be used for real-time detection and monitoring of the system in the whole process of system invasion. This paper puts forward an intrusion detection system under IPv6 platform based on intrusion detection feature attribute reduction by using pattern matching, so as to expand the range of application and user group of the security products. By the analysis and comparison of various pattern matching algorithms, the new algorithm realizes the intrusion feature module matching under IPv6, and make detection system be of high efficiency. Later experiments have proved this view.

Anomaly Intrusion Detection Based on Concept Lattice

2012 ◽  
Vol 220-223 ◽  
pp. 2388-2392
Author(s):  
Li Fang Wang
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Concept Lattice ◽  
Attribute Reduction ◽  
Training Data ◽  
Detection Model ◽  
Detection Technology ◽  
Anomaly Intrusion Detection

In order to identify potential and effective intrusion detection rules, and improve the detection rate of intrusion detection system, this paper combines the concept lattice with intrusion detection technology and proposes a anomaly intrusion detection system based on concept lattice theory. The system first pre-treats those collected data, regulates data and builds concept lattice using the minimal set of attributes which are obtained by attribute reduction. And it analyzes the implication relations between concepts and obtains non-redundant classification rules. The anomaly intrusion detection model based compared with other tests can easily get training data. Experimental results show the model reduces the computation amount to achieve classification, improves the intrusion detection rate and effectively controls the false detection rate.

An Improved Pattern Matching Algorithm in the Intrusion Detection System

2011 ◽  
Vol 48-49 ◽  
pp. 203-207 ◽  
Author(s):  
Ping Zhang ◽  
Jiang Hui Liu
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
Intrusion Detection System ◽  
Detection System ◽  
Typical Pattern ◽  
Matching Algorithm ◽  
Pattern Matching Algorithm

This paper proposed a matching algorithm FBMH(Fast Boyer Moor Horspool),which made an improvement on the BMH(Boyer Moor Horspool) and BMHS(Boyer Moor Horspool Sundy) matching algorithm based on the study of several typical pattern matching algorithms used in intrusion detection. The result shows that, the FBMH algorithm has less intrusion detection matching time than BMH and BMHS algorithm. The FBMH algorithm accelerated the speed of pattern matching effectively, therefore enhanced the efficiency of the intrusion detection system.

Research on Dynamic Intrusion Detection Model Based on Risk Coefficient

2010 ◽  
Vol 129-131 ◽  
pp. 124-127 ◽  
Author(s):  
Zheng Wei ◽  
Jun Yi Hou ◽  
Hua Tan ◽  
Guang Nan Guo
Keyword(s):  
Intrusion Detection ◽  
Detection Method ◽  
System Call ◽  
Security Technology ◽  
Detection Model ◽  
Risk Coefficient ◽  
Detection Technology ◽  
System Risk ◽  
Effectiveness And Efficiency ◽  
Definition Of

Intrusion detection technology is a kind of network security technology that can protect system from attacks. Based on the definition of system call risk coefficient, the paper brought out a system risk coefficient based dynamic intrusion detection model. Using the model, the drawbacks of traditional intrusion detection method based on system call was solved, which speeds up detection process and decreased false rate and error rate. It can also effectively identify error operations or users. The experiment result also proves the effectiveness and efficiency of the method.

Study on Intrusion Detection System Based on Data Mining

2015 ◽  
Vol 713-715 ◽  
pp. 2499-2502
Author(s):  
Jiang Kun Mao ◽  
Fan Zhan
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection Efficiency ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Cluster Center ◽  
Security Technology ◽  
Learning Techniques

Intrusion detection system as a proactive network security technology, is necessary and reasonable to add a static defense. However, the traditional exceptions and errors detecting exist issues of leakage police, the false alarm rate or maintenance difficult. In this paper, The intrusion detection system based on data mining with statistics, machine learning techniques in the detection performance, robustness, self-adaptability has a great advantage. The system improves the K-means clustering algorithm, focus on solving two questions of the cluster center node selection and discriminating of clustering properties, the test shows that the system further enhance the detection efficiency of the system.

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