Authors:
Sherif Saad
1
;
William Briguglio
1
and
Haytham Elmiligi
2
Affiliations:
1
School of Computer Science, Windsor University and Canada
;
2
Computing Science Department, Thompson Rivers University and Canada
Keyword(s):
Malware, Machine Learning, Behaviour Analysis, Adversarial Malware, Online Training, Detector Interpretation.
Related
Ontology
Subjects/Areas/Topics:
Internet Technology
;
Intrusion Detection and Response
;
Web Information Systems and Technologies
Abstract:
In this paper, we argue that detecting malware attacks in the wild is a unique challenge for machine learning techniques. Given the current trend in malware development and the increase of unconventional malware attacks, we expect that dynamic malware analysis is the future for antimalware detection and prevention systems. A comprehensive review of machine learning for malware detection is presented. Then, we discuss how malware detection in the wild present unique challenges for the current state-of-the-art machine learning techniques. We defined three critical problems that limit the success of malware detectors powered by machine learning in the wild. Next, we discuss possible solutions to these challenges and present the requirements of next-generation malware detection. Finally, we outline potential research directions in machine learning for malware detection.