Hai Thanh Nguyen
Bakgrunn og aktiviteter
Assoc. Prof. Dr. Hai Thanh Nguyen (male) is currently leading the Blockchain-related projects at CollectiveAI ASA and the AI/ML projects at Vnnor ASA. Before 2020, Dr. Nguyen has been working at Telenor Research on big data and machine learning for more than 6 years, where he was leading several AI/ML projects on cyber security, fraud detection, recommendation systems and IoTs for Telenor. In 2007, he got a master degree in computer science and applied mathematics from Moscow State University. In 2012, he got a PhD degree on machine learning and artificial intelligence for security and digital forensics at Norwegian University of Science and Technology (NTNU) Gjovik, Norway. He is also holding an Adjunct Associate Professor position at the Department of Computer Science, NTNU Trondheim, Norway. His main interests are artificial intelligence/machine learning, information security, cryptography and blockchain.
Vitenskapelig, faglig og kunstnerisk arbeid
Et utvalg av nyere tidsskriftspublikasjoner, kunstneriske produksjoner, bok, inklusiv bokdeler og rapport-del. Se alle publikasjoner i databasen
- (2020) Cophylogenetic analysis of the relationship between anemonefish Amphiprion (Perciformes: Pomacentridae) and their symbiotic host anemones (Anthozoa: Actiniaria). Marine Biology Research. vol. 16 (2).
- (2019) Visual analytics for exploring air quality data in an AI-enhanced IoT environment. MEDES '19: Proceedings of the 11th International Conference on Management of Digital EcoSystems.
- (2019) New Ideas in Ranking for Personalized Fashion Recommender Systems. Business and Consumer Analytics: New Ideas.
- (2018) BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Information Sciences. vol. 453.
- (2017) A big data analytics approach to combat telecommunication vulnerabilities. Cluster Computing. vol. 20 (3).
- (2016) Better Protection of SS7 Networks With Machine Learning. 2016 6th International Conference on IT Convergence and Security (ICITCS).
- (2016) A network based IMSI Catcher detection. 2016 6th International Conference on IT Convergence and Security (ICITCS).
- (2015) Detecting IMSI-catcher using soft computing. Communications in Computer and Information Science. vol. 545.
- (2013) Enhancing the effectiveness of Web Application Firewalls by generic feature selection. Logic Journal of the IGPL. vol. 21 (4).
- (2012) Comprehensive analysis of spectral minutiae for vein pattern recognition. IET Biometrics. vol. 1 (1).
- (2012) Optimal and robust communication for a uniform source. IET Communications. vol. 6 (6).
- (2012) Combining expert knowledge with automatic feature extraction for reliable web attack detection. Security and Communication Networks.
- (2012) Generic feature selection measure for botnet malware detection. Proceedings of the 12th International Conference on Intelligent Systems Design and Applications (ISDA); 27 - 29 November 2012; Kochi, India.
- (2012) A General Lp-norm Support Vector Machine via Mixed 0-1 Programming. Machine Learning and Data Mining in Pattern Recognition, 8th International Conference; MLDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings.
- (2012) Adaptive Intrusion Detection System via Online Machine Learning. Proceedings of the 12th International Conference on Hybrid Intelligent Systems (HIS); 4-7 December 2012; Pune, India.
- (2012) Feature Extraction Methods for Intrusion Detection Systems. Threats, Countermeasures, and Advances in Applied Information Security.
- (2012) Reliability in A Feature-Selection Process for Intrusion Detection. Reliable Knowledge Discovery.
- (2011) Improving Effectiveness of Intrusion Detection by Correlation Feature Selection. International Journal on Mobile Computing and Multimedia Communications. vol. 3 (1).
- (2011) On General Definition of L1-norm Support Vector Machines for Feature Selection. International Journal of Machine Learning and Computing. vol. 1 (3).
- (2011) A General L1-norm Support Vector Machine for Feature Selection. Proceedings of 2011 3rd International Conference on Machine Learning and Computing.