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Using deep learning to make businesses more secure.

Businesses are facing a major challenge: how to stay cyber secure in an evolving threat landscape.

The growth of the Internet of Things (IoT) means that there are now thousands, if not more, endpoints contributing to corporate networks. Depending on the business, the IoT can account for millions of additional devices, creating an unprecedented magnitude of exposure.

And this trend is only going to continue. Analyst house Gartner predicts that there will be 20.4 billion connected devices by 2020, yet worryingly, less than half of UK firms are able to detect IoT breaches.

At the same time, cyber-criminals are becoming increasingly targeted and more sophisticated in their attacks. Instead of taking a scatter-gun approach, evidence suggests that criminals are identifying vulnerable organisations and attacking them repeatedly. Furthermore, they’re collaborating in marketplace environments, sharing tips, tricks and advice on how to launch damaging attacks on enterprises.

Cyber-security teams are on the front line of defence, but they’re under increasing pressure to do more with less and are often overstretched and overworked.

The fact that attackers spend an average of six months within a network clearly indicates that when it comes to enterprise anomaly detection, a change is needed.

Changing the approach to network security

Network security traditionally relies on legacy, rule-based machine learning methods to detect possible attacks. This approach relies on passing historical known malicious data into the learning algorithm, for example past anomalous activity that was suspicious or malicious and required blocking. In other words, the system is told what to look for, and flags accordingly to security teams.

The problem with this method is that detection is restricted to previously seen threats, ultimately hindering an organisation’s ability to investigate activity not seen before, causing them to miss new attacks.

Uncovering the unknown with deep learning

Businesses can’t afford to remain ignorant to unusual or unseen behaviours. This is why we developed Noble Vision, which uses deep learning to analyse billions of data points, identifying anomalous activities in your organisation in real-time.

Deep learning transforms network security from a passive system that is fed seen behaviour, to an active solution that can detect threats in real-time and uncover things not seen before.

Rather than working off rules, deep learning network monitoring is based on unsupervised learning algorithms which continuously learn an organisation’s behaviour to detect anomalies, and proactively look for abnormal behaviour.

By actively and continuously learning from network activity, the technology has the ability to become smarter over time and improve future judgements. As a result, your business will improve its ability to keep pace with even the most evasive and advanced methods of attack, protecting your organisation in the ongoing battle against cyber-criminals.

When applied to cyber-security, deep learning can outpace criminals and deal with threats before they evolve into a potentially devastating attack. If you want to transform your network security today, get in touch!


Get in touch with any questions or to request a demo of our technology