AdamW+: Machine Learning Framework to Detect Domain Generation Algorithms for Malware-Reference-Cited by-同舟云学术

AdamW+: Machine Learning Framework to Detect Domain Generation Algorithms for Malware

Published:2024 Issue: Volume:12 Page:79138-79150
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Javed Awais¹^ORCID,Rashid Imran¹^ORCID,Tahir Shahzaib¹^ORCID,Saeed Saqib²^ORCID,Almuhaideb Abdullah M.³^ORCID,Alissa Khalid³^ORCID

Affiliation:

1. College of Signals, National University of Sciences and Technology (NUST), Islamabad, Pakistan

2. Saudi Aramco Cybersecurity Chair, Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

3. Saudi Aramco Cybersecurity Chair, Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Funder

Saudi Aramco Cybersecurity Chair, Imam Abdulrahman Bin Faisal University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10542099.pdf?arnumber=10542099

Reference41 articles.

1. Strategically-motivated advanced persistent threat: Definition, process, tactics and a disinformation model of counterattack

2. Detecting domain-generation algorithm (DGA) based fully-qualified domain names (FQDNs) with Shannon entropy;Dorian Wong;arXiv:2304.07943,2023

3. Learning-Based Detection of Malicious Hosts by Analyzing Non-Existent DNS Responses

4. A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach

5. Paint It Black: Evaluating the Effectiveness of Malware Blacklists