A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer

2020 ◽  
Vol 148 ◽  
pp. 113249 ◽  
Author(s):  
Hadeel Alazzam ◽  
Ahmad Sharieh ◽  
Khair Eddin Sabri
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

scholarly journals A feature selection algorithm combining information gain and multi-objective genetic search for intrusion detection system

2021 ◽  
Vol 336 ◽  
pp. 08008
Author(s):  
Tao Xie
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Information Gain ◽  
Detection System ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Genetic Search ◽  
Multi Objective

In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features to find the optimal feature combination. We classified the Kddcup98 dataset into five classes, DOS, PROBE, R2L, and U2R, and conducted numerous experiments on each class. Experimental results show that for each class of attack, the proposed algorithm can not only speed up the feature selection, but also significantly improve the detection rate of the algorithm.

Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs

2019 ◽  
Vol 497 ◽  
pp. 77-90 ◽  
Author(s):  
K Selvakumar ◽  
Marimuthu Karuppiah ◽  
L SaiRamesh ◽  
SK Hafizul Islam ◽  
Mohammad Mehedi Hassan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Rough Set ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Fuzzy Rough Set

A Novel Cloud Intrusion Detection System Using Feature Selection and Classification

2015 ◽  
Vol 11 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Anand Kannan ◽  
Karthik Gururajan Venkatesan ◽  
Alexandra Stagkopoulou ◽  
Sheng Li ◽  
Sathyavakeeswaran Krishnan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Detection Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Positive Rate

This paper proposes a new cloud intrusion detection system for detecting the intruders in a traditional hybrid virtualized, cloud environment. The paper introduces an effective feature selection algorithm called Temporal Constraint based on Feature Selection algorithm and also proposes a classification algorithm called hybrid decision tree. This hybrid decision tree has been developed by extending the Enhanced C4.5 algorithm an existing decision tree based classifier. Furthermore, the experiments conducted on the sample Cloud Intrusion Detection Datasets (CIDD) show that the proposed cloud intrusion detection system provides better detection accuracy than the existing work and reduces the false positive rate.

Sign in / Sign up

Export Citation Format

Share Document