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
Recommended Citation
Urag, O. I. (2018). A framework on predicting network based IDS alerts. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/5531