Fast & Autonomous

FORENSIC, a platform anomalies detection in Cyber Physical Systems

What is FORENSIC?

For

Manufacturers and integrators of Cyber Physical Systems (CPS).

Who

Are dissatisfied with current software-based intrusion detection method and associated high computation overheads.

Offer

Affordable & runtime anomaly detection.

Custom hardware-based and AI-powered product, FORENSIC, detects anomalies by collecting low-level hardware features of the system using a non-intrusive approach.

Our Solution
  • A hardware-software codesign-based approach.
  • Create a custom hardware-based autonomous monitoring interface.
  • AI classifier to detect anomaly based on extracted features.
  • Threat detection is based on robust device features that are difficult to compromise.
  • Integrates seamlessly with the existing technology stack.
Why Choose Us
  • Robust.

    FORENSIC is hard to tampering.
  • Non-intrusive.

    The FORENSIC does not alter processor behaviour
  • Independent.

    FORENSIC is independent of internal S/W modelling.
  • Efficient.

    FORENSIC enables fast detection with low power consumption.
  • Tolerability.

    FORENSIC allows to manage increase of devices.

Video demonstration

Our Team
Sangeet Saha
Lecturer

Dr Sangeet Saha is a Lecturer in the Embedded and Intelligent Systems Laboratory at the University of Essex and will lead this project. He has been part of several large industrial, EPSRC, EU funded research projects as the main researcher for developing Embedded Systems, Debug infrastructure and SoC solutions. He is the recipient of an early career research award from YERUN (Young European Research University Network).

Xiaojun Zhai
Senior Lecturer

Dr Xiaojun Zhai is a Senior lecturer in the Embedded Intelligent Systems Laboratory at the University of Essex and will contribute technical expertise on non-intrusive debug interfacing and machine learning experience in this project. He has successfully led a number of EPSRC, Innovate UK, Royal Society projects as PI/Co-I.

Klaus McDonald-Maier
Professor

Professor Klaus McDonald-Maier, semiconductor pioneer, entrepreneur and a world leading authority on the development of diagnostic capability for semiconductor systems is University Professor of Embedded Systems Technology and Head of Research at Essex University where he leads the 60+ Robotics and Embedded Systems Research group. He has delivered significant economic impact for the UK from EPSRC funded research with two successful startup companies, including : UltraSoC Technologies, a semiconductor debug IP company which he co-founded, led the technology development, introduced a highly disruptive technology successfully into the marketplace, grew the company to 60+ staff, helping raise over $20 million in VC funds and enabled successful acquisition by Siemens (Mentor Graphics) in 2020.

Michal Borowski
Research officer

Michal is a programming enthusiast and a computer science student at the University of Essex. His main research area is embedded systems security. Currently, he is a Research officer in the Embedded and Intelligent Systems Laboratory. Prototype development and validation is his main responsibility for the FORENSIC project.

Xuqi Zhu
Research officer

Mr. Xuqi is a research officer in the Embedded and Intelligent Systems Laboratory at the University of Essex. His research interests include AI-based embedded system design, secure hardware design, and deep learning for edge devices. Currently, he is assisting the team in market research, prototyping scoping, and patent preparation for FORENSIC.

Flavia Popescu-Richardson
Knowledge Exchange Manager

Add description

We were in
Embedded and Intelligent Systems Laboratory (EIS@Essex)

We focus on security, power, performance and reliability, advanced embedded systems and processor architectures targeted for cyber physical systems, automotive/industrial, robotics, Internet of Things and real-time critical systems.

Contacts
  • sangeet.saha@essex.ac.uk
  • xzhai@essex.ac.uk
  • kdm@essex.ac.uk
Address
  • University of Essex
  • Wivenhoe Park
  • Colchester CO4 3SQ