Mining network data for intrusion detection through combining SVMs with ant colony networks

2014 ◽  
Vol 37 ◽  
pp. 127-140 ◽  
Author(s):  
Wenying Feng ◽  
Qinglei Zhang ◽  
Gongzhu Hu ◽  
Jimmy Xiangji Huang
Keyword(s):  
Intrusion Detection ◽  
Ant Colony ◽  
Network Data

scholarly journals Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 11-19
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Detection System ◽  
Ant Colony ◽  
Detection Accuracy ◽  
Ant Colony Optimization Algorithm ◽  
Network Intrusion ◽  
Secure Network ◽  
Main Component

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

Research and Implementation of Intrusion Detection Based Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 946-951 ◽  
Author(s):  
Guang Cai Cui ◽  
Bai Tong Liu
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Real Time ◽  
Original Data ◽  
Network Data ◽  
Initial Population ◽  
Classification Rules ◽  
Detection Technology ◽  
Network Intrusions ◽  
Data Source

For traditional intrusion detection technology, the lack of intelligent and self-adaptive has become increasingly prominent when they cope with unknown attacks. A method based on genetic algorithm was presented for discovering and learning the intrusion detection rules. This algorithm uses the network data packet as an original data source, after pretreatment, initialized them to be the initial population of the genetic algorithm, then derive the classification rules. These rules were used to detect or classify network intrusions in a real-time network environment, selecting the intrusion packets. The experiment proves the efficiency of the presented method.

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