Machine learning in cybersecurity: what is it and what do you need to know?

Recent breakthroughs in machine learning and artificial intelligence mean AI-enabled technologies are gaining traction. The billion-dollar cybersecurity industry is no exception, as vendors begin to scale and automate their processes intelligently - all while locked into the early stages of a security arms race with professional hackers. A recent report from analyst firm ABI Research estimates that machine learning in cybersecurity will enormously bolster spending in big data, intelligence and analytics, reaching as much as $96 billion (£71.9 billion) by 2021. Vendors are likely to find buyers in large enterprises, and more than likely, across industries that are especially prone to attack: think government and defence, banking, and across the technology sector. At the moment, ABI's report says, User and Entity Behavioural Analytics - using machine learning for threat detection by analysing data at scale - is the driving force. "Using static machine learning models to detect previously unknown malware is the only use case I'm aware of that offers clear evidence of effective results," says cybersecurity analyst at 451 Research, Adrian Sanabria.