Faster and More Accurate Malware Detection Through Predictive Machine Learning: Correlating Static and Behavioral Features

Decades even before the buzz went off, machine learning has proven its ability to decipher information from vast datasets to see hard-to-spot patterns, classify and cluster data, as well as make predictions using algorithms. With its myriad of real-life applications, cybersecurity remains to be one of its top use areas: It gives traditional cybersecurity solutions the edge it needs to catch destructive threats such as ransomware before it gets deployed in a system, which saves organizations' time, money, and reputations. Traditional machine learning largely deals with historical knowledge. It allows computers to make inferences based on datasets that have been previously labeled by humans. In cybersecurity, training a machine learning model to learn what malicious files and programs look like can help in the discovery of new, emerging, or unclassified threats via correlation.