Professor Christopher Leckie
- Data Mining, Network Intrusion Detection, Artificial Intelligence for Telecommunications, Bioinformatics
Chris Leckie is a Professor in the Department of Computing and Information Systems at The University of Melbourne.
- Artificial Intelligence (AI)
- 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.
- 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.
- Liu L, Kan A, Leckie C, Hodgkin P. Comparative evaluation of performance measures for shading correction in time-lapse fluorescence microscopy.. J Microsc. 2017, Vol. 266, Issue 1.
- 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.
- Anwar T, Liu C, Vu HL, Leckie C. Partitioning road networks using density peak graphs: Efficiency vs. accuracy. INFORMATION SYSTEMS. Pergamon. 2017, Vol. 64.
- Moshtaghi M, Leckie C, Karunasekera S. A framework for distributed data analysis for IoT. Internet of Things: Principles and Paradigms. 2016.
- 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.
- 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.
- 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.
- Lyu L, Law YW, Monazam Erfani S, Leckie C, Palaniswami M. An Improved Scheme for Privacy-Preserving Collaborative Anomaly Detection. 2016 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATION WORKSHOPS (PERCOM WORKSHOPS). IEEE. 2016.
- Yang M, Rajasegarar S, Sridhara Rao A, Leckie C, Palaniswami M. Anomalous behavior detection in crowded scenes using clustering and spatio-temporal features. IFIP Advances in Information and Communication Technology. 2016, Vol. 486.
- Ghafoori Z, Monazam Erfani S, Rajasegarar S, Karunasekera S, Leckie C. Anomaly detection in non-stationary data: Ensemble based self-adaptive OCSVM. International Joint Conference on Neural Networks (IJCNN). 2016, Vol. 2016-October.
- Kowsar Y, Moshtaghi M, Velloso E, Kulik L, Leckie C. Detecting unseen anomalies in weight training exercises. 28th Australian Conference on Computer-Human Interaction (OzCHI). 2016.
- Nguyen X, Chan J, Romano S, Bailey J, Leckie C, Kotagiri R, Pei J. Discovering outlying aspects in large datasets. DATA MINING AND KNOWLEDGE DISCOVERY. Kluwer Academic Publishers. 2016, Vol. 30, Issue 6.
- Zameni M, Moshtaghi M, Leckie C. Efficient Query Processing on Road Traffic Network. 2016 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATION WORKSHOPS (PERCOM WORKSHOPS). IEEE. 2016.
- Salehi M, Leckie C, Bezdek J, Vaithianathan T, Zhang X. Fast Memory Efficient Local Outlier Detection in Data Streams. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. IEEE Computer Society. 2016, Vol. 28, Issue 12.
View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile