Intelligent Algorithms for Effective Malware Detection and Classification

Abstract

In every aspect of life, digital technology plays a significant part. Computers and newlinesmartphones become our havens for accessing information, developing, articulating, newlineconnecting, and cooperating, allowing us to maintain a sustainable degree of personal, newlineinterpersonal, and professional existence. As the genuine use of the virtual realm newlinehas risen, so have the opportunities for unethical individuals, such as scammers, newlineextortionists, vandals, and other fraudsters, to profit from malware development. newlineWhile the real objective for malware creators is to generate a great deal of money newlinesurreptitiously, they also have other objectives such as activism and pranks, spying, newlinedigital theft, and other major offenses such as security breaches. The most popular newlinedesktop and mobile operating systems,Windows and Android have long been profitable newlinetargets for malware writers, and a huge number of malware programs exploit victims newlineevery day. To keep up with the growing attack surface, effective and generalized antimalware newlinesystems are required to detect even zero-day malware and resolve incidents newlinewith the least manual intervention. Although various malware detection methods have newlinebeen developed, effective detection for a wide range of incoming samples remains a newlineserious challenge. Cyber attackers are constantly using advanced features such as code newlineobfuscation to develop malware variants and evade detection by traditional malware newlinedetection techniques. Even though the classifier has been trained with existing variants newlinefrom the same class, classifying novel malware variants with similar features into their newlinerelevant classes is a challenging task. The identification and extraction of unique newlinefeatures for each malware is yet another issue for generalizing the malware detection newlinesystem. Conventional Malware Detection Systems (MDS) such as static and dynamic newlinemethodologies are both unsuccessful in evaluating and identifying complex and zeroday newlinemalware. newline