A heuristic clustering algorithm for intrusion detection based on information entropy

2006 ◽  
Vol 11 (2) ◽  
pp. 355-359 ◽  
Author(s):  
Xiong Jiajun ◽  
Li Qinghua ◽  
Tu Jing
Keyword(s):  
Intrusion Detection ◽  
Information Entropy ◽  
Clustering Algorithm ◽  
Heuristic Clustering

A New Information Entropy-Based Ant Clustering Algorithm

2011 ◽  
Vol 87 ◽  
pp. 101-105
Author(s):  
Wei Li Zhao ◽  
Zhi Guo Zhang ◽  
Zhi Jun Zhang
Keyword(s):  
Information Entropy ◽  
Clustering Algorithm ◽  
Synthetic Data ◽  
Real Data ◽  
Misclassification Error ◽  
Data Sets ◽  
New Information ◽  
Misclassification Error Rate ◽  
Heuristic Clustering ◽  
Entropy Functions

Ant-based clustering is a heuristic clustering method that draws its inspiration from the behavior of ants in nature. We revisit these methods in the context of a concrete application and introduce some modifications that yield significant improvements in terms of both quality and efficiency. In this paper, we propose a New Information Entropy-based Ant Clustering (NIEAC) algorithm. Firstly, we apply new information entropy to model behaviors of agents, such as picking up and dropping objects. The new entropy function led to better quality clusters than non-entropy functions. Secondly, we introduce a number of modifications that improve the quality of the clustering solutions generated by the algorithm. We have made some experiments on real data sets and synthetic data sets. The results demonstrate that our algorithm has superiority in misclassification error rate and runtime over the classical 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.

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