Professor Christopher Leckie

  • Room: Level: 07 Room: 7.11
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Data Mining, Network Intrusion Detection, Artificial Intelligence for Telecommunications, Bioinformatics

Personal webpage

http://scholar.google.com/citations?user=wUsI0cAAAAAJ

Biography

Chris Leckie is a Professor in the Department of Computing and Information Systems at The University of Melbourne.

Research interests
- Artificial Intelligence (AI)
- telecommunications
- machine learning, fault diagnosis, distributed systems and design automation 

Prof 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.

Recent publications

  1. Tang Z, Kuijper M, Chong MS, Mareels I, Leckie C. Linear system security—Detection and correction of adversarial sensor attacks in the noise-free case. Automatica. Pergamon-Elsevier Science. 2019, Vol. 101. DOI: 10.1016/j.automatica.2018.11.048
  2. Yan M, Chan C, Gygax AF, Yan J, Campbell L, Nirmalathas A, Leckie C. Modeling the total energy consumption of mobile network services and applications. Energies. MDPIAG. 2019, Vol. 12, Issue 1. DOI: 10.3390/en12010184
  3. Ganji M, Chan J, Stuckey PJ, Bailey J, Leckie C, Kotagiri R, Park L. Semi-supervised blockmodelling with pairwise guidance. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2019, Vol. 11052 LNAI. DOI: 10.1007/978-3-030-10928-8_10
  4. Zameni M, He M, Moshtaghi M, Ghafoori Z, Leckie C, Bezdek JC, Kotagiri R. Urban sensing for anomalous event detection: Distinguishing between legitimate traffic changes and abnormal traffic variability. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2019, Vol. 11053 LNAI. DOI: 10.1007/978-3-030-10997-4_34
  5. Rathore P, Ghafoori Z, Bezdek J, Palaniswami M, Leckie C. Approximating Dunn's Cluster Validity Indices for Partitions of Big Data. IEEE Transactions on Cybernetics. Institute of Electrical and Electronics Engineers. 2018, Vol. PP. DOI: 10.1109/TCYB.2018.2806886
  6. Leckie C, Tang Z, Kuijper M, Mareels I. Attack correction for noise-free linear systems subject to sensor attacks. 23rd International Symposium on Mathematical Theory of Networks and Systems. 2018.
  7. Yang M, Rashidi L, Rajasegarar S, Leckie C, Sridhara Rao A, Palaniswami M. Crowd activity change point detection in videos via graph stream mining. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2018, Vol. 2018-June. DOI: 10.1109/CVPRW.2018.00059
  8. Fahiman F, Disano S, Monazam Erfani S, Mancarella P, Leckie C. Data-Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks. IEEE Transactions on Power Systems. IEEE - Institute of Electrical and Electronic Engineers. 2018. DOI: 10.1109/TPWRS.2018.2884711
  9. Kumar D, Ghafoori Z, Bezdek J, Leckie C, Kotagiri R, Palaniswami M. Dealing with Inliers in Feature Vector Data. INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS. World Scientific Publishing Co. 2018, Vol. 26. DOI: 10.1142/S021848851840010x
  10. Weerasinghe S, Alpcan T, Monazam Erfani S, Leckie C, Pourbeik P, Riddle J. Deep learning based game-theoretical approach to evade jamming attacks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2018, Vol. 11199 LNCS. DOI: 10.1007/978-3-030-01554-1_22
  11. Zhang X, Salehi M, Leckie C, Luo Y, He Q, Zhou R, Kotagiri R. Density biased sampling with locality sensitive hashing for outlier detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2018, Vol. 11234 LNCS. DOI: 10.1007/978-3-030-02925-8_19
  12. Miao Y, Pan L, Rajasegarar S, Zhang J, Leckie C, Xiang Y. Distributed detection of zero-day network traffic flows. Communications in Computer and Information Science. Springer. 2018, Vol. 845. DOI: 10.1007/978-981-13-0292-3_11
  13. Ghafoori Z, Monazam Erfani S, Rajasegarar S, Bezdek J, Karunasekera S, Leckie C. Efficient Unsupervised Parameter Estimation for One-Class Support Vector Machines. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. Institute of Electrical and Electronics Engineers. 2018, Vol. 29, Issue 10. DOI: 10.1109/TNNLS.2017.2785792
  14. Kumar D, Wu H, Rajasegarar S, Leckie C, Krishnaswamy S, Palaniswami M. Fast and Scalable Big Data Trajectory Clustering for Understanding Urban Mobility. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. IEEE - Institute of Electrical and Electronic Engineers. 2018, Vol. 19, Issue 11. DOI: 10.1109/TITS.2018.2854775
  15. Yang M, Rashidi L, Rajasegarar S, Leckie C. Graph stream mining based anomalous event analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2018, Vol. 11012 LNAI. DOI: 10.1007/978-3-319-97304-3_68

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile