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Machine Learning a Force-Multiplier in Malware Prevention

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Every day over 1 million new viruses are discovered on the internet. The sheer volume of threats is due to the ever increasing prevalence of advanced malware designed to look completely unique in the eyes of signature-based anti-virus.  Polymorphic malware produces multiple unique versions of a malicious file by automatically rearranging characters, inserting whitespace and obfuscating code. Even slight changes to a file in this manner will result a completely unique hash that will enable the malware to slip by traditional signature AV solutions.

How can Machine Learning Help?

Given the magnitude of the threat, defense against malware requires the ability analyze potentially harmful files quickly and at volume. We can use rules and algorithms to automate the process of looking for common malicious commands in the code of a file, but the ever-changing nature of malware means security analysts must constantly adapt these rules to detect new and emerging threats. Using machine learning is key to making this possible.

Machine learning helps security analysts identify emerging malware threats by aiding in the analysis and classification process of files. When a file is analyzed, the code is tokenized to strip it of common operators used in polymorphism and then classified using several machine learning algorithms that have been trained to identify evolving tends in malware. This enables analysis to occur on a cluster of files deemed to be similar, as opposed to one at a time, providing a breadth of protection against emerging malware strains. Multiple machine learning algorithms should be used in tandem to aide in accuracy, and reduce false positives.

With the release of Fireware 12.0 WatchGuard is introducing a new lightweight GAV detection engine supplemented by machine-learning modules. WatchGuard customers will enjoy improved efficacy of their GAV service driven by:

To learn more visit, watchguard.com/wgrd-products/security-services/gateway-antivirus

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