Machine learning and deep learning are the buzzwords of the day in cybersecurity. And while those are terms on the minds of many in the industry, there’s still a lot of work to do to improve understanding machine learning – and why to use it.
You might ask – how is this relevant to MSPs or MSSPs? But machine and deep learning speaks to something all of us are looking for from our solutions: we want it to work, and we want it to work well. When applied appropriately, deep learning capabilities – which we’ll talk about below – allow your solutions to “think” faster than the human mind, and these technologies can be trained to spot malicious behaviors and other tells that indicate when your system or end users are at risk, catching zero-day threats before they can do any harm. Trends show that machine learning is the wave of the future, and while clients may not be fully aware of it yet, getting ahead of the curve as machine and deep learning advance with leaps and bounds in cybersecurity is a way to stay ahead of the trend and be ready to assess new options for keeping your clients secure.
Machine Learning Defined
Machine learning, at its most basic definition, is exactly what it sounds like – the ability for a program to learn. These artificial intelligence programs can be “trained” to get better at predicting outcomes. For the purposes we’re discussing here, those outcomes are predicting and preventing cyberattacks, malware, ransomware, and more. Different types of machine learning require more or less human interaction to do the training. Supervised machine learning, for example, requires human input, providing the data the programs need to learn from in order to improve.
Deep learning on the other hand allows the program to review data and make its own conclusions without human intervention. Deep learning is top of mind for cybersecurity professionals because of its effectiveness with concepts like fraud or threat detection or weeding out spam.
Traditional antivirus, while still helpful, simply can’t keep up with the sheer number of threats created every single day. By the time an antivirus solution is updated, countless pieces of malware have already been created and unleashed on the world. The exact number of viruses created in 2017 varies a bit depending on the research, but we know that more than three million viruses were created every day in 2015 and 2016, and by some accounts, 2017 saw new malware every four or five seconds. It’s an astounding pace.
Deep Learning's Value
This is where machine learning and deep learning come in. Traditional machine learning uses input from expert threat analysis to direct the training – which not only slows down the training process, but also allows for an element of subjectivity and human error. Deep learning, on the other hand, with its ability to learn and improve without human interaction to “train” it means a cybersecurity solution using deep learning can spot and stay ahead of new malware, rather than waiting on signatures or input from patches or updates.
It also increases the ability to analyze and process more samples to help get better at spotting previously unknown malware. For example: Sophos just released an update introducing deep learning to its anti-ransomware, anti-exploit solution, Intercept X, now allowing for the solution to process hundreds of millions of samples, enabling it to predict and stop malware faster and more accurately than standard machine learning.
Deep learning has additional benefits above and beyond more standard machine learning, as well. Because it functions more like a human brain, it’s able to analyze complex relationships between different inputs, while doing so faster than the human brain can operate. It’s also more scalable than standard machine learning, and lighter as well.
More and more, the industry is realizing that predictive technology is what will keep data safe. The threats evolve and appear faster than reactive defenses can keep up. We need technology to help keep up with a threat landscape that moves faster than the human mind. According to a recent ESG Research Report, the rapid changes to the threat landscape are the primary reason cybersecurity is harder today than before.
We need next-gen solutions to stay ahead of online threats that only seem to get faster and more complex with every passing day. The automated, predictive abilities of machine and deep learning technologies are becoming the new, best weapon against threats that appear faster than the human mind can keep up with.