In 1959, an IBM employee by the name of Arthur Samuel programmed a computer to play checkers against him. Over time, the program was able to collect data, strategise and win a game all by itself. And thus, was born.
More than half a century on, there have been huge developments in and capabilities for mass consumption – from computer games and self-driving cars, to facial recognition and curing cancer. In the world of cybersecurity, it is only recently that and has made its mark by predicting and pro actively stopping attacks before they start. With the number of hacks, attacks and breaches growing every year, and has come in the nick of time. But in the last 12 months, we have seen almost every antivirus vendor and his sales engineer tout the latest in ‘revolutionary’, -powered endpoint protection. And the noise is deafening. So, what is really and, more importantly, what isn’t it when it comes to endpoint security?
Let’s get one thing straight: techniques are not signatures, nor do they use signatures to operate. A signature is a set of instructions written by a human or, at best, written by machines once they are given a set of rules created by a human. Signatures cannot strategise, generalise, or make decisions that lie outside of the set ‘rule book’.
Legacy AV products that rely on signatures cannot identify malware that is not already known and threats that have not already been reported as malware. In other words, a ‘Patient Zero’ must first get infected for that malware to be discovered. Once the malware is identified, more time elapses as a signature is generated (akin to a mugshot being uploaded to a police database) before this information is sent to the antivirus product’s knowledge base to protect other customers. It’s like locking the stable door after a thief has stolen your prized racehorse.
Unlike products that react after the damage has been done, true techniques do not use signatures to identify what is or is not malicious. This means, it offers the unique ability to block malicious software in milliseconds, before it has a chance to execute, even if it has never been seen previously.
Think of it like teaching a child: rather than feeding the child all the answers, you teach them how to think for themselves. Machine learning builds on the human brain and outdoes it by millions of additional data points, allowing it to manage the overwhelming volume, variety and velocity of today’s cyberthreats.
Developers begin by feeding millions of malicious and non-malicious samples into a supercomputer that, over time and through supervised learning, begins to understand the nature and intentions of each file and trains itself to discern good files from bad. They do not have to verify findings with the cloud or wait for humans to determine a course of action once a breach has been identified. They can identify, decide, and act autonomously without human intervention. And with years of training, the engine has the potential to reach 99.99 per cent efficacy – highly accurate in its own right and even more so when compared to the 50-60 per cent accuracy offered by signature-based endpoint security. […]