MEGR-APT: A Memory-Efficient APT Hunting System Based on Attack Representation Learning-Reference-Cited by-同舟云学术

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MEGR-APT: A Memory-Efficient APT Hunting System Based on Attack Representation Learning

Published:2024 Issue: Volume:19 Page:5257-5271
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Aly Ahmed¹^ORCID,Iqbal Shahrear²^ORCID,Youssef Amr³^ORCID,Mansour Essam¹

Affiliation:

1. Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada

2. National Research Council Canada, Fredericton, NB, Canada

3. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10517754.pdf?arnumber=10517754

Reference68 articles.

1. Threat hunting, definition and framework;Liliengren,2018

2. A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities

3. Stuxnet: Dissecting a Cyberwarfare Weapon

4. ATLAS: A sequence-based learning approach for attack investigation;Alsaheel

5. Provenance-based Intrusion Detection Systems: A Survey

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