Development of an efficient classifier using proposed sensitivity-based feature selection technique for intrusion detection system

2018 ◽  
Vol 10 (1) ◽  
pp. 80
Author(s):  
H.S. Hota ◽  
Dinesh K. Sharma ◽  
A.K. Shrivas
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Technique ◽  
Selection Technique

scholarly journals Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Cybersecurity ◽  
2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Raisa Abedin Disha ◽  
Sajjad Waheed
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Performance Analysis ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Experimental Result ◽  
Feature Selection Technique ◽  
Selection Technique

AbstractTo protect the network, resources, and sensitive data, the intrusion detection system (IDS) has become a fundamental component of organizations that prevents cybercriminal activities. Several approaches have been introduced and implemented to thwart malicious activities so far. Due to the effectiveness of machine learning (ML) methods, the proposed approach applied several ML models for the intrusion detection system. In order to evaluate the performance of models, UNSW-NB 15 and Network TON_IoT datasets were used for offline analysis. Both datasets are comparatively newer than the NSL-KDD dataset to represent modern-day attacks. However, the performance analysis was carried out by training and testing the Decision Tree (DT), Gradient Boosting Tree (GBT), Multilayer Perceptron (MLP), AdaBoost, Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the binary classification task. As the performance of IDS deteriorates with a high dimensional feature vector, an optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique. This technique employed Gini impurity as the splitting criterion of trees and adjusted the weights for two different classes of the imbalanced data to make the learning algorithm understand the class distribution. Based upon the importance score, 20 features were selected from UNSW-NB 15 and 10 features from the Network TON_IoT dataset. The experimental result revealed that DT performed well with the feature selection technique than other trained models of this experiment. Moreover, the proposed GIWRF-DT outperformed other existing methods surveyed in the literature in terms of the F1 score.

scholarly journals Robust Feature Selection Technique for Intrusion Detection System

2018 ◽  
Vol 11 (2) ◽  
pp. 33-44 ◽  
Author(s):  
Pullagura Indira Priyadarsini ◽  
Manikonda Srininivasa Sesha Sai ◽  
Akula Suneetha ◽  
Munnangi Velengini Bala Teresa Santhi
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Technique ◽  
Selection Technique
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