Intrusion Detection Model Based on Improved Support Vector Machine

2010 ◽  
Author(s):  
Jingbo Yuan ◽  
Haixiao Li ◽  
Shunli Ding ◽  
Limin Cao
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Support Vector ◽  
Detection Model ◽  
Model Based ◽  
Improved Support Vector Machine

Network Intrusion Detection Model Based on Genetic Algorithm Optimizing Parameters of Support Vector Machine

2014 ◽  
Vol 989-994 ◽  
pp. 2012-2015
Author(s):  
Chun Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Intrusion Detection ◽  
Computer Security ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Detection Model ◽  
Model Based ◽  
Network Intrusion ◽  
Parameters Selection

Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Parameters selection of support vector machine is a important problems in network intrusion detection. In order to improve network intrusion detection precision, this paper proposed a network intrusion detection model based on parameters of support vector machine (SVM) by genetic algorithm. The performance of the model was tested by KDD Cup 99 data. Compared with other network intrusion detection models, the proposed model has significantly improved the detection precision of network intrusion.

An SVM-Based Ensemble Approach for Intrusion Detection

2019 ◽  
Vol 14 (1) ◽  
pp. 66-84 ◽  
Author(s):  
Santosh Kumar Sahu ◽  
Akanksha Katiyar ◽  
Kanchan Mala Kumari ◽  
Govind Kumar ◽  
Durga Prasad Mohapatra
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Learning Algorithm ◽  
Majority Voting ◽  
Support Vector ◽  
Detection Model ◽  
Ensemble Approach ◽  
Efficient Learning

The objective of this article is to develop an intrusion detection model aimed at distinguishing attacks in the network. The aim of building IDS relies on upon preprocessing of intrusion data, choosing most relevant features and in the plan of an efficient learning algorithm that properly groups the normal and malicious examples. In this experiment, the detection model uses an ensemble approach of supervised (SVM) and unsupervised (K-Means) to detect the patterns. This technique first divides the data and forms two clusters as per K-Means and labels the clusters using the Support Vector Machine (SVM). The parameters of K-Means and SVM are tuned and optimized using an intrusion dataset. The SVM provides up to 88%, and K-Means provides up to 83% accuracy individually. However, the ensemble of K-Means and SVM provides more than 99% on three benchmarked datasets in less time. The SVM only classifies three instances of each cluster randomly and labels them as per a majority voting approach. The proposed approach outperforms compared to earlier ensemble approaches on intrusion datasets.

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