PACES Providing Autonomous Capabilities for Evolving SCADA (EPSRC 2012-2015)


SCADA (Supervisory Control And Data Acquisition) has proved to be a powerful and successful technology. Across the world SCADA systems are deeply integrated into the large scale infrastructures used in power generation, power transmission, radioactive waste processing, manufacturing, refining, water treatment, space exploration and various military applications. Traditionally SCADA implementations adopt a centralised architecture, either across one single geographical site, or across multiple sites using proprietary communications protocols. In spite of this trend to distributed solutions the supervisory control function continues to be performed at a centralised location, typically supervised by trained personnel using compliant HMI (Human-Machine Interface) tools. This centralised architecture presents overwhelming problems for system designers needing to integrate ever more diverse components and to scale to larger, more complex deployments. Moreover, this increasing complexity can realistically only be achieved through the adoption of autonomous or intelligent system components that can replace the supervisory function provided by humans.

More recent trends show SCADA systems incorporating widely available COTS (Commercial Off-The-Shelf) software to deliver functionality and adopting recognised communications standards such as TCP/IP to facilitate integration and remote administration. The use of COTS increases available functionality and robustness but introduces new vulnerabilities. Attackers can exploit their knowledge of such widely available components and attacks can be 'designed' in ways previously not possible with the earlier proprietary systems. Closely linked to security is the need for fault tolerance. Here too we must develop intelligent SCADA systems that can self-monitor and detect anomalous behaviour (resulting from malicious attack or component fault) and invoke response that protects the goals of the whole system.

The next generation of SCADA systems must develop a set of autonomous and intelligent capabilities to address a number of pressing requirements. Problems presented by increasing process complexity, advances in sensor technologies, the increasing demand for integration with other enterprise solutions, increasingly inadequate security protection and a higher required standard of fault tolerance must all be solved. To provide solutions to these problems the proposed research focuses on the development of a novel Multi-Agent System (MAS) architecture. This architecture is integrated with an advanced event reasoning framework that can fully exploit sensor data and domain knowledge, including treatment of inherent uncertainties, incompleteness and inconsistency to autonomously infer system state and crucially to inform human and autonomous decision makers in the system.

Increased autonomy presents new challenges of system security. The next generation of autonomous SCADA must detect, diagnose and respond in real-time to security breaches and anomalous behaviours. The proposed research exploits new Deep Packet Inspection capabilities and network traffic analysis to develop a unique 'cyber-sensor', providing visibility of overall system health and integrity to human operators and autonomous components. Brought together, these novel research outputs will equip the next generation of autonomous SCADA systems with the capabilities to respond in real-time to evolving situations, self-awareness of changes and abnormal behaviours, fault and noise tolerance, and real-time decision support.

Other details

  • Period: October 2012- September 2015
  • Type: Research Project
  • Status: Current
  • Funding Body: EPSRC 623,033.00

    Personnel Involved

  • Prof Weiru Liu
  • Prof Carles Sierra
  • Prof Lluis Godo
  • Dr Jun Hong
  • Dr Michael Loughlin
  • Prof Sakir Sezer
  • Dr. Kim Bauters (Research Fellow)
  • Dr. Yingke Chen (Research Fellow)
  • Ms Sarah Calderwood (PhD student)
  • Mr Kevin McAreavey (Researcher)
  • Dr Jianbing Ma (Visitor) (Bournemouth University)