PACES Providing Autonomous Capabilities for Evolving SCADA (EPSRC 2012-2016)





Summary

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- March 2016
  • 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
  • Ms Sarah Calderwood (PhD student)
  • Mr Ronan Killough (PhD student)
  • Dr Kevin McAreavey (Research Fellow)
  • Dr Ivan Palomares Carrascosa (Research Fellow)

    Associated members:

  • Dr. Kim Bauters (Ex-Research Fellow)
  • Dr. Yingke Chen (Ex-Research Fellow)
  • Dr Jianbing Ma (Visitor) (Coventry University)

    Outputs

    The PACES team produced 22 research papers and five demos covering a wide range of applications.

  • Bauters, K., McAreavey, K., Liu, W., Hong, J. Godo, L. and Sierra, C. (2017) Managing Different Sources of Uncertainty in a BDI Framework in a Principled way with Tractable Fragments. Journal of Aetificial Intelligence Research (to appear).

  • McAreavey, K., Bauters, K., Liu, W., Hong, J. (2017) The Event Calculus in Probabilistic Logic Programs with Annotated Disjunctions Proceedings of the 16th International Conference on Antonomous Agents and Multiagent Sytems (AAMAS'17). to appear.

  • Calderwood, S., McAreavey, K., Liu, W., and Hong, J. (2017) Modelling and Reasoning with Uncertain Event-Observations for Event Inference. Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17) (to appear).

  • Palomares, I., Bauters, K., Liu, W., and Hong, J. (2016) A Two-Stage Online Approach for Collaborative Multi-Agent Planning under Uncertainty. In Proceedings of the 10th International Conference on Scalable Uncertainty Management (SUM'16):214-229. 2016.

  • Palomares, I., Killough, R., Bauters, K., Liu, W., and Hong, J. (2016) A collaborative multi-agent framework based on online risk-aware planning and decision-making. In Proceedings of the 28th International Conference on Tools with Artificial Intelligence (ICTAI'16).

  • Dubois, D., Liu, W., Ma, J. and Prade, H. (2016) The basic principles of uncertain information fusion. An organised review of merging rules in different online risk-aware planning and decision-making. In ICTAI’16. representation frameworks. Journal of Information Fusion, 32:12-39.

  • Calderwood, S, McAreavey, K. Liu, W., Hong, J. (2016) Context-dependent Combination of Sensor Information in Dempster-Shafer Theory for BDI. Knowledge and Information Systems Journal (KAIS). DOI: 10.1007/s10115-016-0978-0

  • Bauters, K., Liu, W., and Godo, L.(2016). Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs. Proceedings of the 9th International Symposium on Foundations of Information and Knowledge Systems (FoIKS'16), 24-41.

  • Calderwood, S, McAreavey, K. Liu, W., Hong, J. (2016) Uncertain Information Combination for Decision Making in Smart Grid BDI Agent Systems. Int. J. of Industrial Control Systems Security (IJICSS), 1(1):21-30, 2016.

  • Killough, R., Bauters, K., McAreavey, K., Liu, W., and Hong, J. (2016) Risk-aware Planning in BDI Agents. Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART'16). to appear.

  • Calderwood, S, McAreavey, K. Liu, W., Hong, J. (2015) Contextual Merging of Uncertain Information for Better Informed Plan Selection in BDI Systems. World Congress on Industrial Control Systems Security (WCICSS'15). Institute of Electrical and Electronics Engineers (IEEE).

  • Bauters, K., McAreavey, K. Hong, J., Chen, Y., Liu, W.., Godo, L., and Sierra, C.(2015) Probabilistic Planning in AgentSpeak using the POMDP framework. in Book: Combinations of Intelligent Methods and Applications, Editor(s) I. Hatzilygeroudis, V. Palade, and J. Prentzas. (This is an extended version of CIMA-14 paper)

  • Ma, J., Liu, W. and Dubois, D.(2015) Rational Partial Choice Functions and Their Application to Belief Revision. Proceedings of the 8th International Conference on Knowledge Science, Engineering and Management (KSEM15). Springer-Verlag LNAI.

  • Bauters, K., Liu, W.., Hong, J., Sierra, C., and Godo, L. (2014) CAN(Plan)+:Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information. Proceedings of the 30th International Conference on Uncertainty in Artificial Intelligence (UAI'14):52-61. July 23-27, 2014, Quebec, Canada.

  • KcAreavey, K. Liu, W., and Miller, P. (2014) Computational Approaches to Finding and Measuring Inconsistency in Arbitrary Knowledge Bases. International Journal of Approximate Reasoning 55(8):1659-1693, 2014.

  • Bauters, K., Liu, W., Hong, J., Godo, L., and Sierra, C.(2014) A Syntactic Approach to Revising Epistemic States with Uncertain Inputs. Proceedings of the 26th International Conference on Tools with Artificial Intelligence (ICTAI'14). 2014.

  • Ma, J., Liu, W., Hong, J., Godo, L., and Sierra, C.(2014) Plan Selection for Probabilistic BDI Agents. Proceedings of the 26th International Conference on Tools with Artificial Intelligence (ICTAI'14). 2014.

  • Calderwood, S., Bauters, K., Liu, W., and Hong, J. (2014) Adaptive uncertain information fusion to enhance plan selection in BDI agent systems. Proceedings of the 4th International Workshop on Combinations of Intelligent Methods and Applications (CIMA'14). 2014.

  • Chen, Y., Bauters, K., Liu, W., Hong, J., McAreavey, K., Sierra, C. & Godo, L. (2014) AgentSpeak+: AgentSpeak with Probabilistic Planning Proceedings of the 4th International Workshop on Combinations of Intelligent Methods and Applications (CIMA'14).

  • Chen, Y., Hong, J., Liu, W., Godo, L., Sierra C., and Loughlin, M. (2013) Incorporating PGMs into a BDI architecture. The 16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA'13), December 2013, New Zealand.

  • Dubois, D., Liu, W., Ma, J., and Prade, H (2013) Towards a general framework for information fusion. The 10th International Conference on Modeling Decisions for Artificial Intelligence (MDAI'13), November 2013. Barcelona, Spain.

  • Calderwood, S., Liu, W., Hong, J., and Loughlin, M. (2013) An Architecture of a Multi-Agent System for SCADA - dealing with uncertainty, plans and actions. Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO'13):300-306. July, 2013, Iceland.