Our founders invented Cyber-Physical threat detection

Research is the foundation of our organisation. We started our company by helping others find innovative approaches that bridge the cybersecurity gap in cyber-physical systems. It led us to develop revolutionary technology.
- Dr Ryan Heartfield, CTO

Our Background

Exalens are thought leaders in AI-driven cybersecurity, cyber-physical systems security, and incident response optimisation.

Over the last 10 years, our people have developed state-of-the-art cyber-physical security monitoring technologies and helped build and deliver innovative cybersecurity solutions across a wide range of emerging and digitally transforming industries.

18+

UK/EU R&D projects awarded

We have delivered ground-breaking innovation in cyber threat detection and response for critical industries undergoing significant digital transformation such as manufacturing, energy, maritime, transport, smart cities, healthcare, and more.

67%

PhDs and Masters

Our team includes experts with PhDs and Masters in disciplines spanning Data Science, Mathematics, Network and Robotic Security.

300+

Published Research Articles

The people of Exalens have published over 300 research articles for advances in AI for Cybersecurity, Intrusion Detection, Network Security, Industrial and Robotic Security, Information Trustworthiness, and other areas.

Our Research Partners comprise of some of Industry’s and Academia’s foremost technology pioneers and innovators

Research Summary

Impact-Aware Response

Autonomous incident response workflows that continuously assess and connect cyber and physical incidents while measuring and tracking risk to provide impact-aware recommendations for response Exalens combines multiple AI approaches to autonomously link and classify correlated cyber, physical, and cyber-physical incidents.

Researcher
Dr. Ryan Heartfield, Sadaiyandi Ramadoss
Researcher

Predictive Condition Monitoring

Real-time, adaptive monitoring for physical machine and process anomalies that require no human intervention or understanding of underlying data Securely reads and upcycles physical machine data from existing OT systems, IoT platforms, devices, and sensors. An anomaly detection engine that self-configures and calibrates its behavioural analysis to best fit any physical machine and process, regardless of data type, frequency, or context.

Researcher
Dr. Ryan Heartfield, Sadaiyandi Ramadoss
Researcher

Sequential Analysis

A detection approach that intelligently replicates human analysis strategies and processes. Combines heuristics, and statistical analysis with state-of-the-art AI methods such as deep learning, and unsupervised and supervised ML, to detect cyber and physical system threats with high efficacy.Provides explainable detection indicators in natural language that supports easy interpretation and actionability for both IT and OT teams.

Researcher
Dr. Ryan Heartfield, Sadaiyandi Ramadoss
Researcher

Dynamic Structural and Lexical embeddings for AGD Detection

Applying Deep Natural Language learning to classify domains names similar to those used malware command and control channels

Researcher
Dr Pankhuri Jain, Dr. Ryan Heartfield
Researcher

Recursive multipass semantic analysis for device similarity and change detection

Identify similar devices and determine when there are sudden changes in behaviour between them

Researcher
Dr. Pankhuri Jain, Dr. Ryan Heartfield
Researcher

SDN-based Resilient Smart Grid Architecture

An architecture for dynamic risk assessment, intrusion detection / correlation, and self-healing in Smart Grids

Researcher
Dr. Orestis Mavropoulos
Researcher
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