Project Description

Professor Chris Leckie
Professor Chris LeckieUniversity of Melbourne

Professor Chris Leckie

University of Melbourne

Dimension 5

Cybersecurity Standards, Organisations and Technologies CV

Chris Leckie is an Associate Professor in the Department of Computing and Information Systems at The University of Melbourne and he is the Director of the Academic Centres Of Cyber Security Excellence (Accse) Program at the University of Melbourne and Associate Director of the Oceania Cyber Security Centre.

For more than 20 years he has contributed to knowledge through award winning teaching and high- quality research, publishing extensively in leading academic journals and conferences. The impact of his work is demonstrated by the fact that it has attracted more than 10,000 citations.

His research interests are:

  • Artificial Intelligence (AI)

  • Telecommunications

  • Machine learning, fault diagnosis, distributed systems and design automation

Associate Professor Leckie has a strong interest in developing AI techniques for a variety of applications in telecommunications, such as network intrusion detection, network management, fault diagnosis and wireless sensor networks. He also has an interest in scalable data mining algorithms for tasks such as clustering and anomaly detection with applications in bioinformatics.

He holds a Doctorate in Philosophy from University of Melbourne and a Bachelor of Engineering (Honours) and a Bachelor of Science from Monash University.

Work in Practice

His practical experience includes working in the telecommunications industry and representing the University of Melbourne on Australian Government international trade missions and cyber delegations with the Ambassador for Cyber Affairs.

Noteworthy Mentions

Chris took part in the Official Australian Government sponsored cyber delegation to Israel, which featured representatives from the Australian government, industry and academia. The Counter Terrorism Ethics whose mission is to “conduct interdisciplinary research on a range of pressing public policy concerns arising from the emergence of global terrorism and is funded under the European Research Council Advanced Grants scheme and is a collaborative enterprise involving some of the world’s leading research institutions and counter-terrorism experts.

In 2019, Chris was a mission participant in the Australian Cyber Security Mission to the USA along with other key Australian cybersecurity industry partners and members of the wider cyber community. The purpose of the mission was to bring the delegation of around 30 organisations (representing the best of the Australian cyber security ecosystem) to the US with the view to engage on partnerships and business opportunities.

Dimension 5 Journal Publications

  • Erfani, S.M., Rajasegarar, S., Karunasekera, S. and Leckie, C., 2016. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognition, 58, pp.121-134.  This paper won the Best Paper Award for Pattern Recognition journal for 2016.

    Abstract: “In cyber security, a major challenge for security analytics is to detect abnormal or suspicious activities using a machine learning technique called anomaly detection. High-dimensional problem domains pose significant challenges for anomaly detection. The presence of irrelevant features can conceal the presence of anomalies. This problem, known as the ‘curse of dimensionality’, is an obstacle for many anomaly detection techniques. Building a robust anomaly detection model for use in high-dimensional spaces requires the combination of an unsupervised feature extractor and an anomaly detector. While one-class support vector machines are effective at producing decision surfaces from well-behaved feature vectors, they can be inefficient at modelling the variation in large, high-dimensional datasets. Architectures such as deep belief networks (DBNs) are a promising technique for learning robust features. We present a hybrid model where an unsupervised DBN is trained to extract generic underlying features, and a one-class SVM is trained from the features learned by the DBN. Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient. The experimental results show that our proposed model yields comparable anomaly detection performance with a deep autoencoder, while reducing its training and testing time by a factor of 3 and 1000, respectively”.

  • Sharma, R., Chan, C.A. and Leckie, C., 2020, June. Evaluation of Centralised vs Distributed Collaborative Intrusion Detection Systems in Multi-Access Edge Computing. In 2020 IFIP Networking Conference (Networking) (pp. 343-351). IEEE.

  • Sharma, R., Chan, C.A. and Leckie, C., 2020, June. Evaluation of Centralised vs Distributed Collaborative Intrusion Detection Systems in Multi-Access Edge Computing. In 2020 IFIP Networking Conference (Networking) (pp. 343-351). IEEE.

  • Han, Y., Hubczenko, D., Montague, P., De Vel, O., Abraham, T., Rubinstein, B.I., Leckie, C., Alpcan, T. and Erfani, S., 2019. Adversarial reinforcement learning under partial observability in software-defined networking. arXiv preprint arXiv:1902.09062.

  • Tang, Z., Kuijper, M., Chong, M.S., Mareels, I. and Leckie, C., 2019. Linear system security—Detection and correction of adversarial sensor attacks in the noise-free case. Automatica, 101, pp.53-59.

  • Han, Y., Rubinstein, B.I., Abraham, T., Alpcan, T., De Vel, O., Erfani, S., Hubczenko, D., Leckie, C. and Montague, P., 2018, October. Reinforcement learning for autonomous defence in software-defined networking. In International Conference on Decision and Game Theory for Security (pp. 145-165). Springer, Cham.

  • Calheiros, R.N., Ramamohanarao, K., Buyya, R., Leckie, C. and Versteeg, S., 2017. On the effectiveness of isolation‐based anomaly detection in cloud data centers. Concurrency and Computation: Practice and Experience, 29(18), p.e4169.

  • Salehi, M., Leckie, C., Bezdek, J.C., Vaithianathan, T. and Zhang, X., 2016. Fast memory efficient local outlier detection in data streams. IEEE Transactions on Knowledge and Data Engineering, 28(12), pp.3246-3260.

  • Han, Y., Chan, J., Alpcan, T. and Leckie, C., 2015. Using virtual machine allocation policies to defend against co-resident attacks in cloud computing. IEEE Transactions on Dependable and Secure Computing, 14(1), pp.95-108.

  • Zhou, C.V., Leckie, C. and Karunasekera, S., 2010. A survey of coordinated attacks and collaborative intrusion detection. Computers & Security, 29(1), pp.124-140.

Professor Chris Leckie
Professor Chris LeckieUniversity of Melbourne