Protecting the world’s largest brands from automated threats

Netacea Limited

The challenge

Netacea quickly and accurately detect and protect against malicious bots with a pioneering server-side approach to bot management. Their technology is implemented in minutes and supports a wide range of integrations to protect websites, mobile apps and APIs.

The company wanted to gain a deeper understanding of anomalies in website traffic and find out more about user intent. Developing deep learning capabilities would allow the company to implement new architectures for bot recognition and strengthen their position in the cybersecurity market.

Working with the Knowledge Transfer Partnership has directly influenced several of Netacea’s product innovations over the last two years and ultimately impacted our core offering. We are delighted to report that access to the state-of-the-art research has attributed to the company’s overall strategy, with the development of effective machine-learning-based models for the identification and categorisation of cyber-attacks in real-time.
Andy Still – Chief Technical Officer, Netacea Limited

What we did

Netacea worked with the University of Nottingham to develop a model able to classify malicious activity. We collaborated on a two and a half year Knowledge Transfer Partnership (KTP), helping Netacea to access our state-of-the-art research in data science and machine learning.

Led by KTP Associate David Fricker, we built a deep learning system to detect anomalies in website traffic. To help us understand what was happening, we built artificial intelligence systems to group users and their intent in real time. Being able to discover the intent of the users means that Netacea are able to block a user in real-time or provide their customer with a threat intelligence feed. This adaptable system protects customers from various automated threat attacks.


One of Netacea’s leading differentiators is the amount and accuracy of bots they detect compared to competitive products. The work done by the KTP project has vastly improved the level of detection that they can offer to customers, particularly around higher risk attack types. The project has opened up new business opportunities for product development. The innovations which have been a direct result of the KTP have also dramatically improved the successful onboarding a new customer and decreased the time to value. Amongst these innovations is Netacea’s patented autoblocking technology – Intent Clustering - which introduces more blocking recommendations for the customer while saving both time and money. A standard customer time to value is now in the region of one week, opposed to one month.