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Machine Learning and Big Data Analytics for Cybersecurity

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Helsinki Central Railway Station, Helsinki, Finland - Hsi-Pin Ma)

Cybersecurity is a promising area for AI/ML. In theory, if a machine has access to everything you currently know is bad, and everything you currently know is good, you can train it to find new malware and anomalies when they surface. In practice, there are three fundamental requirements for this to work. First, you need access to data -- lots of it. The more malware and benign samples you have, the better your model will be. Second, you need data scientists and data engineers to be able to build a pipeline to process the samples continuously and design models that will be effective. Third, you need security domain experts to be able to classify what is good and what is bad and be able to provide insights into why that is the case. In my opinion, many companies touting AI/ML-powered security solutions lack one or more of these pillars.


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