Title

A framework on predicting network based IDS alerts

Date of Publication

2018

Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Katrina Ysabel C. Solomon

Defense Panel Chair

Gregory G. Cu

Defense Panel Member

Katrina Ysabel C. Solomon
Fritz Kevin S. Flores
Rafael A. Cabredo

Abstract/Summary

To keep up with the increasing prevalence of cybersecurity attacks, improvements in the current prevention and detection strategies must be made. One of the key areas of interest in improving attack prevention is the application of machine learning techniques to existing alerts being captured by intrusion detection systems (IDS) in order to predict different aspects of future attacks. Much focus has been given by researches to predict the next alert or alert type, however, this information is not enough for making intrusion responses. There have been few researches that tried to enhance the prediction context by including the attacker and victim nodes. This research presents a framework of generating prediction models on intrusion alerts with the inclusion of time in the prediction context.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

CDTG007619

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

1 computer disc ; 4 3/4 in.

Keywords

Intrusion detection systems (Computer security); Computer security

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