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vahidi, J., m, B., ahmadzadeh, M., p, A. (2021). A Comprehensive Semi-Suprvised Model for Collaborative Intrusion Detection Based on Network Behavior Profiling Using The Concept of Deep Learning and Fuzzy Correlation of Alerts along. Electronic and Cyber Defense, 9(3), 165-186.
javad vahidi; b m; mohammad ahmadzadeh; a p. "A Comprehensive Semi-Suprvised Model for Collaborative Intrusion Detection Based on Network Behavior Profiling Using The Concept of Deep Learning and Fuzzy Correlation of Alerts along". Electronic and Cyber Defense, 9, 3, 2021, 165-186.
vahidi, J., m, B., ahmadzadeh, M., p, A. (2021). 'A Comprehensive Semi-Suprvised Model for Collaborative Intrusion Detection Based on Network Behavior Profiling Using The Concept of Deep Learning and Fuzzy Correlation of Alerts along', Electronic and Cyber Defense, 9(3), pp. 165-186.
vahidi, J., m, B., ahmadzadeh, M., p, A. A Comprehensive Semi-Suprvised Model for Collaborative Intrusion Detection Based on Network Behavior Profiling Using The Concept of Deep Learning and Fuzzy Correlation of Alerts along. Electronic and Cyber Defense, 2021; 9(3): 165-186.

A Comprehensive Semi-Suprvised Model for Collaborative Intrusion Detection Based on Network Behavior Profiling Using The Concept of Deep Learning and Fuzzy Correlation of Alerts along

پدافند الکترونیکی و سایبری
Volume 9, Issue 3 - Serial Number 35, December 2021, Pages 165-186    PDF (1.88 M)
Document Type: Original Article
Authors
javad vahidi* 1; b m2; mohammad ahmadzadeh3; a p4
1Assistant Professor, University of Science and Technology, Tehran, Iran
2Associate Professor, University of Science and Technology, Tehran, Iran
3PhD Student, Department of Information Technology Management, Faculty of Management and Economics, Islamic Azad University, Science and Research Branch, Tehran, Iran
4Assistant Professor, Islamic Azad University, Karaj Branch, Karaj, Iran
Receive Date: 11 March 2021,  Revise Date: 07 May 2021,  Accept Date: 08 June 2021 
Abstract
Today, intrusion detection systems are extremely important in securing computers and computer networks. Correlated systems are next to intrusion detection systems by analyzing and combining the alarms received from them, appropriate reports for review and producing security measures. One of the problems face intrusion detection systems is generating a large volume of false alarms, so one of the most important issues in correlated systems is to check the alerts received by the intrusion detection system to distinguish true-positive alarms from false-positive alarms. The main focus of this research is on the applied optimization of classification methods to reduce the cost of organizations and security expert time in alert checking. The proposed Incrimental Intrusion Detetection Model using Correlator (IIDMC) is tested on a valid test dataset and the results show the efficiency of the proposed model and consequently its high accuracy.
Keywords
: Intrusion etection; Fuzzy Correlator; Incremental Online Learning; Active Learnin
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