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Malicious software is abundant in a world of innumerable computer users, whoare constantly faced with these threats from various sources like the internet,local networks and portable drives. Malware is potentially low to high risk andcan cause systems to function incorrectly, steal data and even crash. Malwaremay be executable or system library files in the form of viruses, worms,Trojans, all aimed at breaching the security of the system and compromisinguser privacy. Typically, anti-virus software is based on a signature definitionsystem which keeps updating from the internet and thus keeping track of knownviruses. While this may be sufficient for home-users, a security risk from anew virus could threaten an entire enterprise network. This paper proposes anew and more sophisticated antivirus engine that can not only scan files, butalso build knowledge and detect files as potential viruses. This is done byextracting system API calls made by various normal and harmful executable, andusing machine learning algorithms to classify and hence, rank files on a scaleof security risk. While such a system is processor heavy, it is very effectivewhen used centrally to protect an enterprise network which maybe more prone tosuch threats.

Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks