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. 2019, Vol. 101, pp. 53-59. DOI: 10.1016/j.automatica.2018.11.048
  2. Yan, M.; Chan, CA.; Gygax, AF.; Yan, J.; Campbell, L.; Nirmalathas, A.; Leckie, C. Modeling the total energy consumption of mobile network services and applications. Energies. 2019, Vol. 12, Issue 1. DOI: 10.3390/en12010184
  3. Ganji, M.; Chan, J.; Stuckey, PJ.; Bailey, J.; Leckie, C.; Ramamohanarao, K.; 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). 2019, Vol. 11052 LNAI, pp. 158-174. DOI: 10.1007/978-3-030-10928-8_10
  4. Zameni, M.; He, M.; Moshtaghi, M.; Ghafoori, Z.; Leckie, C.; Bezdek, JC.; Ramamohanarao, K. 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). 2019, Vol. 11053 LNAI, pp. 553-568. DOI: 10.1007/978-3-030-10997-4_34
  5. Rathore, P.; Ghafoori, Z.; Bezdek, JC.; Palaniswami, M.; Leckie, C. Approximating Dunn's Cluster Validity Indices for Partitions of Big Data. IEEE Transactions on Cybernetics. 2018, Vol. PP, pp. 1-13. 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. . Hong Kong University of Science and Technology. 2018.
  7. Yang, M.; Rashidi, L.; Rajasegarar, S.; Leckie, C.; Rao, AS.; 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, pp. 328-336. DOI: 10.1109/CVPRW.2018.00059
  8. Fahiman, F.; Disano, S.; Erfani, SM.; Mancarella, P.; Leckie, C. Data-Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks. IEEE Transactions on Power Systems. 2018. DOI: 10.1109/TPWRS.2018.2884711
  9. Kumar, D.; Ghafoori, Z.; Bezdek, JC.; Leckie, C.; Ramamohanarao, K.; Palaniswami, M. Dealing with Inliers in Feature Vector Data. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems. WORLD SCIENTIFIC PUBL CO PTE LTD. 2018, Vol. 26, Issue Suppl.2, pp. 25-45. DOI: 10.1142/S021848851840010x
  10. Weerasinghe, S.; Alpcan, T.; Erfani, SM.; 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). SpringerLink. 2018, Vol. 11199 LNCS, pp. 386-397. 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. 2018, Vol. 11234 LNCS, pp. 269-284. 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, pp. 173-191. DOI: 10.1007/978-981-13-0292-3_11
  13. Ghafoori, Z.; Erfani, SM.; Rajasegarar, S.; Bezdek, JC.; Karunasekera, S.; Leckie, C. Efficient Unsupervised Parameter Estimation for One-Class Support Vector Machines. IEEE Transactions on Neural Networks and Learning Systems. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2018, Vol. 29, Issue 10, pp. 5057-5070. 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-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2018, Vol. 19, Issue 11, pp. 3709-3722. 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 Nature. 2018, Vol. 11012 LNAI, pp. 891-903. DOI: 10.1007/978-3-319-97304-3_68
  16. Ganji, M.; Chan, J.; Stuckey, PJ.; Bailey, J.; Leckie, C.; Ramamohanarao, K.; Davidson, I. Image constrained blockmodelling: A constraint programming approach. SIAM International Conference on Data Mining, SDM 2018. Society for Industrial and Applied Mathematics. 2018, pp. 19-27.
  17. Moshtaghi, M.; Bezdek, JC.; Erfani, SM.; Leckie, C.; Bailey, J. Online cluster validity indices for performance monitoring of streaming data clustering. International Journal of Intelligent Systems. 2018. DOI: 10.1002/int.22064
  18. Chenaghlou, M.; Moshtaghi, M.; Leckie, C.; Salehi, M. Online Clustering for Evolving Data Streams with Online Anomaly Detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). SPRINGER INTERNATIONAL PUBLISHING AG. 2018, Vol. 10938, pp. 506-519. DOI: 10.1007/978-3-319-93037-4_40
  19. Lim, KH.; Chan, J.; Leckie, C.; Karunasekera, S. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowledge and Information Systems. SPRINGER LONDON LTD. 2018, Vol. 54, Issue 2, pp. 375-406. DOI: 10.1007/s10115-017-1056-y
  20. Han, Y.; Rubinstein, BIP.; Abraham, T.; Alpcan, T.; De Vel, O.; Erfani, S.; Hubczenko, D.; Leckie, C.; Montague, P. Reinforcement learning for autonomous defence in software-defined networking. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 11199 LNCS, pp. 145-165. DOI: 10.1007/978-3-030-01554-1_9

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