How is machine learning used in cyber security?

While rapid advancements have certainly been made in recent years throughout the tech industry, there has also been a continuous development in the sophistication of malware attacks – with perpetrators using techniques that make them even more difficult to spot on even the savviest of devices. In fact, the National Cyber Security Centre (NCSC) reported in their 2021 breaches survey that almost 40% of UK businesses experienced an attack last year alone.

Amongst other efforts, machine learning (ML) is fast becoming a popular trend across organisations to bolster security infrastructure and act as a more forceful deterrent against threats. Already, this transformative technology has played a critical role for businesses in reducing the opportunity for attacks and limiting damage, should they have been exposed to vulnerabilities.

But with the continued advancements in the complexity of cyber fraud, traditional security is no longer fully comprehensive to ensure a system is as secure as possible. Previously unseen methods of attacks are impossible to detect with security protocols that are only looking for known threats.

That’s why here at Central, we’ve partnered with Darktrace to offer industry-leading artificial intelligence systems that allow for proactive monitoring of organisational activities, quickly identifying, triaging, and even neutralising the issue instantly.

So, what exactly is ML?

A subfield of artificial intelligence (AI), ML is a group of techniques and technologies using algorithms and statistical models to scrutinise large volumes of data and discover unique patterns or uncover anomalies, which it can then use to draw inferences and make predictions on new input data.

Similar to the way in which Spotify offers recommendations for new music – based on a user’s previous listening experiences – ML is one of the various approaches to AI that utilises a system that is capable of learning from data and making decisions to build upon this it, without the aid of human interaction.

One area of ML, known as deep learning, imitates the ways in which humans gain certain types of knowledge through artificial neural networks in which algorithms are complexly layered and used to extract progressively accurate iterations of data.

So, what are the applications of ML in cyber security?

With their ability to trawl through endless datasets, identify patterns and pinpoint anomalies, ML systems are proving increasingly useful for proactively uncovering security threats in their infancy and alerting the right people before they have the chance to compromise your systems.

Not only does this AI approach save a lot of precious time and resources, it also afford continuous improvement over time – as you fuel your system with fresh data, the model’s accuracy and efficiency to build upon information will continually improve with subsequent training.

This data training approach does mean that ML is not a rapid start solution to potential threats. For algorithms to be able to set a baseline of ‘normal’ performance, they need to be fed months of data. Only then will the system be capable of discovering patterns and detecting threats and anomalies accurately.

So, ML can make cyber security smoother, more proactive, less costly and far more productive – but only if the underlying supporting data provides an absolute overview, and has been doing so for a generous period of time. If your algorithms are not well designed or developed, the result won’t be very useful.

Exploring the key techniques

There are numerous variations of ML algorithms, but the most common classification techniques are supervised, unsupervised, semi-supervised, and reinforcement learning, which are primarily differentiated by the data they ingest.

Supervised learning: With this method,algorithms are trained using labels examples in which the desired data output is present to prompt pattern recognition and predict any unlabelled values. Supervised learning is primarily used in applications where previous data predicts future events, such as anticipating and identifying a cyber security threat.

Unsupervised learning: Capable of recognising abnormalities in data, without prompts, beyond the examples it has already seen, unsupervised learning constantly scouts the network for anomalies and draws inferences to form a variety of patterns that can be flagged as potential dangers. This method is essential for identifying the ever-changing methods that attackers use in an attempt to compromise your device.

Semi-supervised learning: This approach combines both supervised and unsupervised learning, augmenting labelled data with unlabelled data to build a more robust model. Because it is often less expensive and easier to obtain, semi-supervised learning generally contains a larger proportion of unlabelled data, and those that are labelled are usually more crucial.

Reinforcement learning: This method typically adopts a trial-and-error approach to learning in which autonomous agents interact with a certain environment in an attempt to see which policy yields the greatest rewards. Organisations can utilise reinforcement learning to proactively test the security of their own infrastructure.

Closing thoughts

There is no denying that ML is an exceptionally powerful tool – but what’s important to remember is that it’s no silver bullet. While the technology is certainly evolving at a significant rate, it will only ever be as good as the expertise that drives it – from the human analysts to the data itself.

And with such a complex array of information available, it’s inevitable that business leaders will feel overwhelmed about these emerging prospects. But the pervasive nature of AI – and the increasing sophistication of cyber threats – means that working knowledge is vital to stay on top form.

If you’re intrigued by what you’ve read in the above article, check out our other blog on ‘why artificial intelligence is the key to cyber security,’ for more food for thought.

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