Improved Semi-supervised Fuzzy Clustering Algorithm and Application in Effective Intrusion Detection System

2013 ◽  
Vol 5 (4) ◽  
pp. 689-696
Author(s):  
Guohong Gao ◽  
Guoyi Miao ◽  
Jiaxia Sun ◽  
Yafeng Han
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Clustering ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection System ◽  
Fuzzy Clustering Algorithm

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.

A Cloud Intrusion Detection System Using Novel PRFCM Clustering and KNN Based Dempster-Shafer Rule

2016 ◽  
Vol 6 (4) ◽  
pp. 18-35 ◽  
Author(s):  
Partha Ghosh ◽  
Shivam Shakti ◽  
Santanu Phadikar
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection System ◽  
Cloud Environment ◽  
Dempster Shafer Theory ◽  
Computing Paradigm ◽  
Rule Set ◽  
Shafer Theory

Cloud computing has established a new horizon in the field of Information Technology. Due to the large number of users and extensive utilization, the Cloud computing paradigm attracts intruders who exploit its vulnerabilities. To secure the Cloud environment from such intruders an Intrusion Detection System (IDS) is required. In this paper the authors have proposed an anomaly based IDS which classifies an incoming connection by taking the deviation of it from the normal behaviors. The proposed method uses a novel Penalty Reward based Fuzzy C-Means (PRFCM) clustering algorithm to generate a rule set and the best rule set is extracted from it using a modified approach for KNN algorithm. This best rule set is used in evidential reasoning of Dempster Shafer Theory for classification. The IDS has been trained and tested with NSL-KDD dataset for performance evaluation. The results prove the proposed IDS to be highly efficient and reliable.

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