scholarly journals Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

2016 ◽  
Vol E99.D (7) ◽  
pp. 1810-1821 ◽  
Author(s):  
Aslıhan AKYOL ◽  
Mehmet HACIBEYOĞLU ◽  
Bekir KARLIK
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Hybrid Classifier ◽  
Feature Sets

A Visualization Strategy with a Pentacle and its Application in the Intrusion Detection System

2014 ◽  
Vol 610 ◽  
pp. 647-652
Author(s):  
Jia Xing Li ◽  
Jing Bo Xia ◽  
Yu Liu ◽  
Yu Zhe Huang ◽  
Bin Luo
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Sets ◽  
Visualization Strategy

In this paper, a novel pentacle-based visualization strategy is used to generate numerical feature for network visiting data. A 20-dimensional feature set is built for the 5-class classifier so as to establish an accurate intrusion detection system. By integrating the proposed 20 new features with the built-in feature sets, which consists of 41 features, the new intrusion detection system achieves an accuracy of 99.588%.

scholarly journals Performance Behavior of Intrusion Detection System (Ids) Based On Ensemble Base Classifier (EBC)

2019 ◽  
Vol 8 (11) ◽  
pp. 3030-3034
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Hybrid Approach ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Hybrid Classifier ◽  
Multi Agent

There is a tremendous growth in the area of information technology due to which, network defence is also facing major challenges. The conventional Intrusion Detection System (IDS) is not able to prevent the recent attacks and malwares. Hence, IDS which is an essential component of the network needs to be protected. Data mining introduce to the process of separate hidden, previously unknown and useful information from huge databases. Data Mining based Intrusion Detection System is combined with Multi-Agent System to improve the presentation of the IDS. We combine the classifiers which is the widespread approach, to increase the accuracy of a single classifier. For experimentation purpose, we use a benchmark intrusion detection dataset, which is KDDCup’99 and the accuracy of the classifiers were estimated using 10-fold cross validation method. In this work, we use the feature selection methods, namely Flexible mutual information based feature selection (FMIFS) and hybrid feature selection algorithm (HFS) to evaluate the importance of features. This work provides Support Vector Machine (SVM), Nave Bayes (NB) and Feed Forward Neural Network (FFNN) to classify attack and normal threads as well as to improve the accuracy we ensemble all classifier into single hybrid classifier using Bagging algorithm. The proposed hybrid approach achieves an accuracy rate of 95.11

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