scholarly journals Relevant Feature Selection Model Using Data Mining for Intrusion Detection System

2014 ◽  
Vol 9 (10) ◽  
pp. 501-512 ◽  
Author(s):  
Ayman I. Madbouly ◽  
◽  
Amr M. Gody ◽  
Tamer M. Barakat
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Model ◽  
Relevant Feature ◽  
Using Data

scholarly journals A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1046 ◽  
Author(s):  
Omar Almomani
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Model ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Proposed Model ◽  
Positive Rate

The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.

scholarly journals Statistical based Feature Selection and Ensemble Model for Network Intrusion Detection using Data Mining Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 858-864
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Data Mining Technique ◽  
Chi Square ◽  
Network Intrusion ◽  
Secure Network ◽  
World Information ◽  
Using Data

In today’s world, Information society, computer networks and their interconnected applications are the emerging technologies. Intrusion Detection System (IDS) is used to distinguish the attitude of the network. Now a days, due to frequent and heavy attacks an Network devices, the Intrusion Detection System has become growing and censorious component to secure Network devices. A huge amount of data is needed to build the perfect Intrusion Detection System. This proposed system focuses on feature selection and ensemble of tree based classification methods to build Intrusion Detection System. The implementation of feature selection is fulfilled by using the NSL-KDD dataset. Statistical based feature selection methods such as Pearson's Correlation, Chi-square, Gain ratio and Symmetrical uncertainty are used to generate four modified datasets. By using that modified datasets the tree based Intrusion Detection models are built using J48, REP Tree and simple CART algorithms. To acquire better prediction of accuracy the algorithms J48, REP tree and simple CART are combined using ensemble method and built perfect tree based Intrusion Detection System.

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