Unsupervised Anomaly Intrusion Detection via Localized Bayesian Feature Selection

2011 ◽  
Author(s):  
Wentao Fan ◽  
Nizar Bouguila ◽  
Djemel Ziou
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Anomaly Intrusion Detection

scholarly journals Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 52181-52190 ◽  
Author(s):  
Wajdi Alhakami ◽  
Abdullah ALharbi ◽  
Sami Bourouis ◽  
Roobaea Alroobaea ◽  
Nizar Bouguila
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Bayesian Approach ◽  
Nonparametric Bayesian ◽  
Anomaly Intrusion Detection

scholarly journals An Efficient Anomaly Intrusion Detection Method With Feature Selection and Evolutionary Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 70651-70663 ◽  
Author(s):  
Samira Sarvari ◽  
Nor Fazlida Mohd Sani ◽  
Zurina Mohd Hanapi ◽  
Mohd Taufik Abdullah
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Detection Method ◽  
Evolutionary Neural Network ◽  
Anomaly Intrusion Detection

scholarly journals Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset

2018 ◽  
Vol 18 (3) ◽  
pp. 111-119
Author(s):  
L. Gnanaprasanambikai ◽  
Nagarajan Munusamy
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Network Security ◽  
Intrusion Detection ◽  
The Internet ◽  
Good Decision ◽  
Anomaly Detector ◽  
Anomaly Intrusion Detection ◽  
Traffic Anomaly ◽  
Security Components

Abstract Network security is essential in the Internet world. Intrusion Detection is one of the network security components. Anomaly Intrusion Detection is a type of intrusion detection that captures the intrinsic characteristics of normal data and uses it in the detection process. To improve the performance of specific anomaly detector selecting the essential features of data and generating a good decision rule is important. The paper we present proposes suitable feature extraction, feature selection and a classification algorithm for traffic anomaly intrusion detection in using NSLKDD dataset. The generated rules of classification process are initial rules of a genetic algorithm.

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