A Graph-based Online Feature Selection to Improve Detection of New Attacks

Hajar Dastanpour; Ali Fanian

Volume 14, Issue 2 , July 2022, , Pages 115-130

http://dx.doi.org/10.22042/isecure.2022.14.2.1

Abstract
  Today, intrusion detection systems are used in the networks as one of the essential methods to detect new attacks. Usually, these systems deal with a broad set of data and many features. Therefore, selecting proper features and benefitting from previously learned knowledge is suitable for efficiently ...  Read More

Real-Time intrusion detection alert correlation and attack scenario extraction based on the prerequisite consequence approach

Z. Zali; M. R. Hashemi; H. Saidi

Volume 4, Issue 2 , July 2012, , Pages 125-136

http://dx.doi.org/10.22042/isecure.2013.4.2.4

Abstract
  Alert correlation systems attempt to discover the relations among alerts produced by one or more intrusion detection systems to determine the attack scenarios and their main motivations. In this paper a new IDS alert correlation method is proposed that can be used to detect attack scenarios in real-time. ...  Read More

A hybridization of evolutionary fuzzy systems and ant Colony optimization for intrusion detection

M. Saniee Abadeh; J. Habibi

Volume 2, Issue 1 , January 2010, , Pages 33-46

http://dx.doi.org/10.22042/isecure.2015.2.1.4

Abstract
  A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network ...  Read More