Isolation-based feature Selection for Unsupervised Outlier Detection

Local Kernel Density Ratio-Based Feature Selection for Outlier Detection

Selecting features is an important step of any machine learning task, though most of the focus has been to choose features relevant for classification and regression. In this work, we present a novel non-parametric evaluation criterion for filter-based feature selection which enhances outlier detection. Our proposed method seeks the subset of features that represents the inherent characteristic...

A Parallel Genetic Algorithm Based Method for Feature Subset Selection in Intrusion Detection Systems

Intrusion detection systems are designed to provide security in computer networks, so that if the attacker crosses other security devices, they can detect and prevent the attack process. One of the most essential challenges in designing these systems is the so called curse of dimensionality. Therefore, in order to obtain satisfactory performance in these systems we have to take advantage of app...

Feature Based Unsupervised Intrusion Detection

Abstract—The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of ...

Outlier Detection via Feature Selection Algorithms in Covariance Estimation

A Parallel Genetic Algorithm Based Method for Feature Subset Selection in Intrusion Detection Systems

Intrusion detection systems are designed to provide security in computer networks, so that if the attacker crosses other security devices, they can detect and prevent the attack process. One of the most essential challenges in designing these systems is the so called curse of dimensionality. Therefore, in order to obtain satisfactory performance in these systems we have to take advantage of app...