Deep malware hunter based unrivaled malware detection schema thru cache retrospective empiricism

Abstract

AbstractMalware development has been tremendous in recent decades, posing a severe threat to modern systems and Internet security. Conventional approaches to malware detection focus solely on data stored on hard disc drives. To prevent detection, the attackers put harmful material in volatile memory and attack sensitive confidential data. Furthermore, static signature‐based malware detection approaches require a recognized malware signature database to detect the malware. These efforts, however, failed because the malware rapidly modified its signature and easily evaded detection by packaging, obfuscating, or encrypting. As a consequence, it is vital to extract appropriate memory characteristics and let the deep learning classifier that learns the features improve malware detection accuracy. Therefore, in this paper, we propose a Deep Malware Hunter Based Unrivaled Malware Detection Scheme that skillfully detects known and unknown malware without harming the device. In this way, our Unrivaled Malware Detection Scheme efficiently extracts the necessary features through sharp inspection and classifies the malware correctly and benignly at an early stage before it attacks the device. Thus the proposed framework efficiently provides an accuracy of 99% detection rate in the malware hunter method.