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.

Description