Prof. Hussein A. Abbass


School of Engineering and Information Technology
University of New South Wales - Canberra - Australia

Email: h D abbass A adfa D edu D au

Home


DP140102590: Challenging systems to discover vulnerabilities using computational red teaming


Project Overview

Funding Organisation

Australian Research Council - Discovery Scheme

Funding Years

2014-2016

Chief Investigators

  1. Hussein Abbass, UNSW-Canberra

Associated Research Fellows

  1. Dr. Jiangjun Tang
  2. Dr. George Leu

Associated Research Students

  1. Ms. Aya Hussein, Ph.D., On Going
  2. Ms. Marwa Hassan, M.Sc., Completed

Project Summary

Computational red teaming concerns the design of computational models to role play intelligent adversaries. These adversaries who are determined to exploit a system rely on creative thinking to discover system-level vulnerabilities by challenging system design, implementation or operations. This project closes a gap in the risk assessment literature by designing automated computational red teaming methods to discover vulnerabilities associated with intentional risks.

Project Publications

Book

  1. Abbass H.A. (2015) Computational Red Teaming: Risk Analytics of Big-Data-to-Decisions Intelligent Systems, Springer International Publishing Switzerland. ISBN 978-3-319-08280-6(Hard Cover), 978-3-319-08281-3 (EBook), ISBN-10 3319082809, doi:10.1007/978-3-319-08281-3.

Journal Papers

  1. Tang, J., Leu, G. and Abbass, H.A. (to appear) Networking the Boids is More Robust Against Adversarial Learning, IEEE Transactions on Network Science and Engineering.
  2. Shir Li Wang, Kamran Shafi, Theam Foo Ng, Chris Lokan, Hussein A. Abbass (2017). Contrasting Human and Computational Intelligence Based Autonomous Behaviors in a Blue-Red Simulation Environment. IEEE Transactions on Emerging Topics in Computational Intelligence, 1, 27-40 doi:10.1109/tetci.2016.2641929
  3. Shafi K., Abbass H.A. (2017) A Survey of Learning Classifier Systems in Games, IEEE Computational Intelligence Magazine, 12, 42 55. doi:10.1109/MCI.2016.2627670.
  4. Leu, G. and Abbass, H.A. (2016) A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents, Knowledge-Based Systems, 105, 1-22, August. doi:10.1016/j.knosys.2016.02.012.
  5. Abbass H.A., Leu G., and Merrick K. (2016) A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data, IEEE Access, 4, 2808 - 2830, doi:10.1109/ACCESS.2016.2571058. [Open Access Full Paper available for Download for Free]
  6. Hussein A. Abbass, Eleni Petraki, Kathryn Merrick, John Harvey, Michael Barlow (2016). Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges. Cognitive Computation, 8, 385-408 doi:10.1007/s12559-015-9365-5
  7. Hussein Abbass, Garrison Greenwood, Eleni Petraki (2015). The N -Player Trust Game and its Replicator Dynamics. IEEE Transactions on Evolutionary Computation, 20, 470-474 doi:10.1109/tevc.2015.2484840

Conference Papers

  1. Tang J., Petraki E., Abbass H.A. (2016) Shaping Influence and Influencing Shaping: A Computational Red Teaming Trust-based Swarm Intelligence Model. International Conference on Swarm Intelligence, Bali, Indonesia, Springer International Publishing. 9712: 14-23. [Awarded Best Paper]
  2. Leu, G., & Abbass, H. (2016). Computational Red Teaming in a Sudoku Solving Context: Neural Network Based Skill Representation and Acquisition. In Intelligent and Evolutionary Systems (pp. 319-332). Springer International Publishing.
  3. Eleni Petraki, Hussein A. Abbass (2015). On trust and influence: A computational red teaming game theoretic perspective. the 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) doi:10.1109/cisda.2014.7035644
  4. Leu, G., Tang, J., Abbass, H. (2014). On the role of working memory in trading-off skills and situation awareness in sudoku. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8836, 571-578
  5. Jiangjun Tang, Hussein A. Abbass (2014). Behavioral learning of aircraft landing sequencing using a society of Probabilistic Finite state Machines. 2014 IEEE Congress on Evolutionary Computation (CEC) doi:10.1109/cec.2014.6900597

Outreach Activities