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


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. Kumar D, Bezdek J, Rajasegarar S, Leckie C, Palaniswami M. A visual-numeric approach to clustering and anomaly detection for trajectory data. VISUAL COMPUTER. Springer. 2017, Vol. 33, Issue 3.
  2. Ristanoski G, Soni R, Rajasegarar S, Bailey J, Leckie C. Clustering aided support vector machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10358 LNAI.
  3. Liu L, Kan A, Leckie C, Hodgkin P. Comparative evaluation of performance measures for shading correction in time-lapse fluorescence microscopy. JOURNAL OF MICROSCOPY. Blackwell Science. 2017, Vol. 266, Issue 1.
  4. Moshtaghi M, Monazam Erfani S, Leckie C, Bezdek JC. Exponentially Weighted Ellipsoidal Model for Anomaly Detection. International Journal of Intelligent Systems. John Wiley & Sons. 2017, Vol. 32, Issue 9.
  5. Salehi M, Leckie C, Bezdek JC, Vaithianathan T, Zhang X. Fast Memory Efficient Local Outlier Detection in Data Streams (Extended Abstract). 2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017). IEEE. 2017.
  6. Zhang X, Dou W, He Q, Zhou R, Leckie C, Kotagiri R, Salcic Z. LSHiForest: A Generic Framework for Fast Tree Isolation based Ensemble Anomaly Analysis. 2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017). IEEE. 2017.
  7. Calheiros R, Ramamohanarao K, Buyya R, Leckie C, Versteeg S. On the effectiveness of isolation-based anomaly detection in cloud data centers. Concurrency Computation. John Wiley & Sons. 2017.
  8. Anwar T, Liu C, Vu HL, Leckie C. Partitioning road networks using density peak graphs: Efficiency vs. accuracy. INFORMATION SYSTEMS. Pergamon. 2017, Vol. 64.
  9. Lim K, 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. 2017.
  10. Nguyen X, Monazam Erfani S, Paisitkriangkrai S, Bailey J, Leckie C, Kotagiri R. Training robust models using Random Projection. International Conference on Pattern Recognition. 2017.
  11. Han Y, Chan J, Alpcan T, Leckie C. Using Virtual Machine Allocation Policies to Defend against Co-Resident Attacks in Cloud Computing. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING. IEEE Computer Society. 2017, Vol. 14, Issue 1.
  12. Moshtaghi M, Leckie C, Karunasekera S. A framework for distributed data analysis for IoT. Internet of Things: Principles and Paradigms. 2016.
  13. Han Y, Alpcan T, Chan J, Leckie C, Rubinstein B. A Game Theoretical Approach to Defend Against Co-Resident Attacks in Cloud Computing: Preventing Co-Residence Using Semi-Supervised Learning. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY. IEEE - Institute of Electrical and Electronic Engineers. 2016, Vol. 11, Issue 3.
  14. Kumar D, Bezdek J, Palaniswami M, Rajasegarar S, Leckie C, Havens TC. A Hybrid Approach to Clustering in Big Data. IEEE TRANSACTIONS ON CYBERNETICS. Institute of Electrical and Electronics Engineers. 2016, Vol. 46, Issue 10.
  15. Kumar D, Bezdek J, Rajasegarar S, Palaniswami M, Leckie C, Chan J, Gubbi Lakshminarasimha J. Adaptive Cluster Tendency Visualization and Anomaly Detection for Streaming Data. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA. Association for Computing Machinery (ACM). 2016, Vol. 11, Issue 2.

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