Machine learning is a term used for a type of artificial intelligence that provides computers with the ability to learn without being explicitly being programmed to do so.
Currently, computers are good at applying what we know and have been programmed to perform tasks and calculations the way we tell it to. However, computers hit their limit when it comes to acquiring and processing new information without the additional input from a human being. Do you think a computer would be able to distinguish a sarcastic comment over a genuine one?
Machine learning tries and teaches computers how to learn by itself. We do this by feeding it historical data, models, trends and get it to run it’s own analysis and trials before coming to a conclusion on the data set.
The potential value machine learning means that almost every industry is currently in a race to research and develop it for real-time applications. By adding that ‘intuitive’ element to already mechanic processes could save us time looking for answers that machine learning could help to solve without even programming it to.
Machine learning has a strong value in security.
As a data centre, security is naturally at the front of our minds at all times, and machine learning could prove to be a valuable ally.
With the recent rise in cybercrime, there is an urgent requirement to stay ahead of the game and develop a new and innovative way of keeping our data safe.
Stopping criminals breaching networks is a significant task and identifying criminals that are hiding in a network is just as hard. It’s impossible for them to hide completely so they invest heavily in resources to disguise their tracks. Finding these tracks in large networks is an intensive, time-consuming task – think, needle in the haystack scenario.
Even if you get a whiff of something suspicious, large numbers of genuine interactions and changing patterns make it hard for a human brain to properly comprehend.
This is where the value of machine learning comes into play. Computers are easily able to identify patterns and process large numbers of interactions quickly, the problems lie with deciphering the genuine interactions with the fake ones.
We have used computers to examine our security for ages with the likes of intrusion detection systems. They watch the flow of data through networks and identify signatures that have been related to known security issues. The system will only look for the signatures the programmers have told them to, making new threats problematic. The idea behind machine learning is to teach the machine using known patterns and teach it to identify suspicious behaviour that is typically related to breaches etc. The hope is, in the future, it will be able to differentiate genuine and fake interactions without our input.
Machine learning is already in use and offers genuine security benefits, but it isn’t the kryptonite to preventing your network being breached. As good as machine learning will be, it doesn’t mean we can let our own guards down and hope that it does it’s job. We still need to be vigilant and continue to apply security best practices – machine learning should only be used as one of many methods of mitigation.
Over the next couple years, we’ll see the development and increased application of machine learning in organisations.