Network Intrusion Detection Using Genetic Algorithm and Predictive Rule Mining

2021 ◽  
pp. 143-156
Author(s):  
Utsha Sinha ◽  
Aditi Gupta ◽  
Deepak Kumar Sharma ◽  
Aarti Goel ◽  
Deepak Gupta
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Rule Mining ◽  
Network Intrusion ◽  
Predictive Rule

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.

Study of Intrusion Detection Based on Genetic Algorithm and Rule

2014 ◽  
Vol 602-605 ◽  
pp. 1797-1802
Author(s):  
Li Wen Xu ◽  
Li Juan Qiao ◽  
Xun Yong Ou ◽  
Kun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Information Technology ◽  
Intrusion Detection ◽  
Maintenance Cost ◽  
Network Intrusion Detection ◽  
Technology Usage ◽  
Rule Based ◽  
Efficient Approach ◽  
Network Intrusion ◽  
High Level

The rapid increase of information technology usage demands the high level of security in order to keep the data resources and equipments of the user secure. In this current era of networks, there is an eventual stipulate for development which is consistent, extensible and easily manageable, with low maintenance cost solutions for Intrusion Detection. Network Intrusion Detection based on rules formulation is an efficient approach to classify various types of attack. DoS or Probing attack are relatively more common, and can be detected more accurately if contributing parameters are formulated in terms of rules. Genetic Algorithm is used to devise such rule. It is found that accuracy of rule based learning increases with the number of iteration.

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