A Naive Feature Selection Method and Its Application in Network Intrusion Detection

2010 ◽  
Author(s):  
Tieming Chen ◽  
Xiaoming Pan ◽  
Yiguang Xuan ◽  
Jixia Ma ◽  
Jie Jiang
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Network Intrusion Detection ◽  
Network Intrusion

scholarly journals High-Performance Feature Selection Model for Network Intrusion Detection System

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1595-1597
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Method ◽  
Reduction Process ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Network Intrusions

Network intrusions detection is a continuous vigilant task and to efficiently analyze the traffic in the corporate network to detect network intrusions. The efficiency of the Network Intrusion Detection System (NIDS) performance can be improved by adopting feature selection or reduction process to suit the present day high speed real time networks. This work is focused on identifying the key features of the audit dataset used to build an efficient light-weight NIDS. The NSL KDD dataset is used in this work titled Attribute Richness Based Feature Selection (ARFS) in order to analyze its performance.The obtained results are compared with the Correlation-based Feature Selection (CFS) and Information Gain (IG) feature selection methods. The proposed feature selection method produced better detection rate comparatively.

An Effective Genetic Algorithm-Based Feature Selection Method for Intrusion Detection Systems

2021 ◽  
pp. 102448
Author(s):  
Zahid Halim ◽  
Muhammad Nadeem Yousaf ◽  
Muhammad Waqas ◽  
Muhammad Suleman ◽  
Ghulam Abbas ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Intrusion Detection Systems ◽  
Detection Systems

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.

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