ELMNT: INTELLIGENT MALWARE DETECTION AND CLASSIFICATION USING EXTREME LEARNING MACHINE CLASSIFIER

Abstract

Security breaches due to attacks by malicious software (malware) continue to escalate posing a major
security concern in this digital age. With many computer users, corporations, and governments
affected due to an exponential growth in malware attacks, malware detection continues to be a hot
research topic. Current malware detection solutions that adopt the static and dynamic analysis of
malware signatures and behavior patterns are time consuming and have proven to be ineffective in
identifying unknown malwares in real-time. Recent malwares use polymorphic, metamorphic, and
other evasive techniques to change the malware behaviors quickly and to generate a large number of
new malwares. Such new malwares are predominantly variants of existing malwares, and machine
learning algorithms (MLAs) are being employed recently to conduct an effective malware analysis.
Therefore, this work proposes the combined visualization and deep learning architectures for static,
dynamic, and image processing based hybrid approach applied in a big data environment, which is the
first of its kind toward achieving robust intelligent zero-day malware detection. Overall, this work
paves way for an effective visual detection of malware using a scalable and hybrid extreme learning
machine model named as ELMNet for real-time deployments.