Mrs Zahra Ghafoori
- Active Learning
- Anomaly Detection
- Cyber Security
- Data Mining and Machine Learning
- Unsupervised Learning
Zahra Ghafoori is a postdoctoral research fellow in the University of Melbourne Academic Centre For Cyber Security Excellence. She studied her PhD at the School of Computing and Information Systems, University of Melbourne, where she developed robust and efficient techniques for unsupervised anomaly detection in complex and dynamic environments. Prior to her PhD studies, she worked as a security expert in Informatics Services Corporation, a top-tier IT service provider for Core/Retail Banking Solutions. She has hands-on experience in cybersecurity risk assessment, incident management, and security auditing.
Zahra Ghafoori is interested in designing algorithms that with limited or no feedback on their performance can learn patterns from massive data collections and are robust against adversarial examples.
- 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
- 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
- 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
- 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
- Ghafoori, Z.; Erfani, SM.; Rajasegarar, S.; Karunasekera, S.; Leckie, CA. Anomaly Detection in Non-stationary Data: Ensemble based Self-Adaptive OCSVM. Proceedings of the International Joint Conference on Neural Networks. IEEE. 2016, Vol. 2016-October, pp. 2476-2483. DOI: 10.1109/IJCNN.2016.7727507
- Ghafoori, Z.; Rajasegarar, S.; Erfani, SM.; Karunasekera, S.; Leckie, CA. Unsupervised Parameter Estimation for One-Class Support Vector Machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). SPRINGER-VERLAG BERLIN. 2016, Vol. 9652, pp. 183-195. DOI: 10.1007/978-3-319-31750-2_15
- Ghafoori, Z.; Dehghan, M.; Nourhoseini, M. PPayWord: A secure and fast P2P micropayment scheme for video streaming. Computer Networks and Distributed Systems. Springer International Publishing. 2014, Vol. 428, pp. 79-91. DOI: 10.1007/978-3-319-10903-9_7