G-Means: A Clustering Algorithm for Intrusion Detection

2009 ◽  
pp. 563-570 ◽  
Author(s):  
Zhonghua Zhao ◽  
Shanqing Guo ◽  
Qiuliang Xu ◽  
Tao Ban
Keyword(s):  
Intrusion Detection ◽  
Clustering Algorithm

The Application of Fuzzy Clustering Number Algorithm in Network Intrusion Detection

2013 ◽  
Vol 760-762 ◽  
pp. 2220-2223
Author(s):  
Lang Guo
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Cluster Number ◽  
Data Set ◽  
Network Intrusion ◽  
Correlation Degree ◽  
Indicator Data ◽  
Detection Effect

In view of the defects of K-means algorithm in intrusion detection: the need of preassign cluster number and sensitive initial center and easy to fall into local optimum, this paper puts forward a fuzzy clustering algorithm. The fuzzy rules are utilized to express the invasion features, and standardized matrix is adopted to further process so as to reflect the approximation degree or correlation degree between the invasion indicator data and establish a similarity matrix. The simulation results of KDD CUP1999 data set show that the algorithm has better intrusion detection effect and can effectively detect the network intrusion data.

scholarly journals A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hongchun Qu ◽  
Libiao Lei ◽  
Xiaoming Tang ◽  
Ping Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Base Station ◽  
Simulation Software ◽  
Wireless Sensor ◽  
Sensor Monitoring

For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.

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.

Intrusion Detection Based on Dynamic Gemini Population DE-K-mediods Clustering on Hadoop Platform

2020 ◽  
pp. 2150001
Author(s):  
Wentie Wu ◽  
Shengchao Xu
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Differential Evolution ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection System ◽  
Differential Evolution Algorithm ◽  
Detection Algorithm ◽  
Local Optimum ◽  
Evolution Algorithm

In view of the fact that the existing intrusion detection system (IDS) based on clustering algorithm cannot adapt to the large-scale growth of system logs, a K-mediods clustering intrusion detection algorithm based on differential evolution suitable for cloud computing environment is proposed. First, the differential evolution algorithm is combined with the K-mediods clustering algorithm in order to use the powerful global search capability of the differential evolution algorithm to improve the convergence efficiency of large-scale data sample clustering. Second, in order to further improve the optimization ability of clustering, a dynamic Gemini population scheme was adopted to improve the differential evolution algorithm, thereby maintaining the diversity of the population while improving the problem of being easily trapped into a local optimum. Finally, in the intrusion detection processing of big data, the optimized clustering algorithm is designed in parallel under the Hadoop Map Reduce framework. Simulation experiments were performed in the open source cloud computing framework Hadoop cluster environment. Experimental results show that the overall detection effect of the proposed algorithm is significantly better than the existing intrusion detection algorithms.

D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks

Measurement ◽  
2014 ◽  
Vol 55 ◽  
pp. 212-226 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Amineh Amini ◽  
Nor Badrul Anuar ◽  
Miss Laiha Mat Kiah ◽  
Ying Wah Teh ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Clustering Algorithm ◽  
Wireless Sensor

The Research on Fuzzy Clustering Method Based on Differential Evolution Algorithm in Intrusion Detection

2014 ◽  
Vol 651-653 ◽  
pp. 547-550
Author(s):  
Qi Fan Yang ◽  
Li Na Wang
Keyword(s):  
Intrusion Detection ◽  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
High Detection Rate ◽  
Data Set ◽  
Fuzzy Clustering Method ◽  
Fuzzy C Means Clustering ◽  
Evolution Algorithm ◽  
Final Answer

Fuzzy C-means clustering algorithm (FCM) is widely applied to the intrusion detection. To acquire a better division for intrusion data, a new method (DEFCM) presented in the paper which combines FCM and differential evolution algorithm (DE) is found application. As a start, several randomly initiated partitions are optimized by FCM, and then the result is provided to differential evolution algorithm. After that, the combined result is sent to FCM again to adjust the partition and obtain the final answer. The method can improve detection performance effectively. The KDDCUP1999 data set is used in the simulation experiment, and the result proves that the DEFCM algorithm has a comparatively high detection rate in intrusion detection.

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