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.
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.
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.
PhDs and Masters
Our team includes experts with PhDs and Masters in disciplines spanning Data Science, Mathematics, Network and Robotic Security.
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.
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.
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.
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.
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
Recursive multipass semantic analysis for device similarity and change detection
Identify similar devices and determine when there are sudden changes in behaviour between them
SDN-based Resilient Smart Grid Architecture
An architecture for dynamic risk assessment, intrusion detection / correlation, and self-healing in Smart Grids