| Analyzing Data Granularity Levels for Insider Threat Detection using Machine Learning DC Le, N Zincir-Heywood, MI Heywood IEEE Transactions on Network and Service Management 17 (1), 30-44, 2020 | 144 | 2020 |
| Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection P Ferreira, DC Le, N Zincir-Heywood International Conference on Network and Service Management, 2019 | 91 | 2019 |
| Anomaly detection for insider threats using unsupervised ensembles DC Le, N Zincir-Heywood IEEE Transactions on Network and Service Management 18 (2), 1152-1164, 2021 | 85 | 2021 |
| Evaluating insider threat detection workflow using supervised and unsupervised learning DC Le, AN Zincir-Heywood 2018 IEEE Security and Privacy Workshops (SPW), 270-275, 2018 | 75 | 2018 |
| Machine learning based insider threat modelling and detection DC Le, AN Zincir-Heywood 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), 1-6, 2019 | 68 | 2019 |
| Exploring anomalous behaviour detection and classification for insider threat identification DC Le, N Zincir‐Heywood International Journal of Network Management, 2020 | 55 | 2020 |
| Data analytics on network traffic flows for botnet behaviour detection DC Le, AN Zincir-Heywood, MI Heywood 2016 IEEE symposium series on computational intelligence (SSCI), 1-7, 2016 | 53 | 2016 |
| On the effectiveness of different botnet detection approaches F Haddadi, D Le Cong, L Porter, AN Zincir-Heywood Information Security Practice and Experience: 11th International Conference …, 2015 | 45 | 2015 |
| Benchmarking evolutionary computation approaches to insider threat detection DC Le, S Khanchi, AN Zincir-Heywood, MI Heywood Proceedings of the genetic and evolutionary computation conference, 1286-1293, 2018 | 37 | 2018 |
| A Frontier: Dependable, Reliable and Secure Machine Learning for Network/System Management DC Le, N Zincir-Heywood Journal of Network and Systems Management, 1-23, 2020 | 26 | 2020 |
| Training regime influences to semi-supervised learning for insider threat detection DC Le, N Zincir-Heywood, M Heywood 2021 IEEE Security and Privacy Workshops (SPW), 13-18, 2021 | 22* | 2021 |
| Exploring adversarial properties of insider threat detection DC Le, N Zincir-Heywood 2020 IEEE Conference on Communications and Network Security (CNS), 1-9, 2020 | 18 | 2020 |
| Unsupervised monitoring of network and service behaviour using self organizing maps DC Le, N Zincir-Heywood, MI Heywood Journal of Cyber Security and Mobility, 15-52, 2019 | 17 | 2019 |
| Dynamic Insider Threat Detection Based on Adaptable Genetic Programming DC Le, AN Zincir-Heywood, MI Heywood IEEE Symposium Series on Computational Intelligence, 2019 | 10 | 2019 |
| Big Data in Network Anomaly Detection DC Le, AN Zincir-Heywood Encyclopedia of Big Data Technologies, 2019 | 5 | 2019 |
| Benchmarking genetic programming in dynamic insider threat detection DC Le, MI Heywood, N Zincir-Heywood Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 2 | 2019 |
| Learning From Evolving Network Data for Dependable Botnet Detection DC Le, N Zincir-Heywood International Conference on Network and Service Management, 2019 | 2 | 2019 |
| A contribution to performance analysis approach of the IEEE 802.11 EDCA in wireless multi-hop networks MT Hoang, M Hoang, DC Le VNU Journal of Science: Computer Science and Communication Engineering 31 (1), 2015 | 2 | 2015 |
Nur Zincir-HeywoodProfessor of Computer Science, Dalhousie UniversityVerified email at cs.dal.ca
