Intrusion Detection Based on Simulated Annealing and Fuzzy C-means Clustering

2009 ◽  
Author(s):  
Jian Wu ◽  
Guo Rui Feng
Keyword(s):  
Simulated Annealing ◽  
Intrusion Detection ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Application of artificial fish swarm optimization semi-supervised kernel fuzzy clustering algorithm in network intrusion

2020 ◽  
Vol 39 (2) ◽  
pp. 1619-1626
Author(s):  
Yongsheng Zong ◽  
Guoyan Huang
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Clustering Algorithm ◽  
Probabilistic Constraints ◽  
Cluster Center ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Network Intrusion ◽  
Fuzzy C Means Clustering ◽  
Artificial Fish Swarm

For the unsupervised learning based clustering algorithm, the intrusion detection rate is low, and the training sample based on supervised learning clustering algorithm is insufficient. A semi-supervised kernel fuzzy C-means clustering algorithm based on artificial fish swarm optimization (AFSA-KFCM) is proposed. Firstly, the kernel function is used to change the distance function in the traditional semi-supervised fuzzy C-means clustering algorithm to define a new objective function, thus improving the probabilistic constraints of the fuzzy C-means algorithm. Then, the artificial fish swarm algorithm with strong global optimization ability is used to improve the KFCM sensitivity to the initial cluster center and easy to fall into the local extremum, thus improving the convergence speed and improving the classification effect. The test results in the Wine and IRIS public datasets show that the AFSA-KFCM clustering algorithm is superior to the traditional algorithm in clustering accuracy and time efficiency. At the same time, the experimental results in KDDCUP99 experimental data show that the algorithm can obtain the ideal detection rate and false detection rate in intrusion detection.

Sign in / Sign up

Export Citation Format

Share Document