N
Nur Zincir-Heywood
Researcher at Dalhousie University
Publications - 73
Citations - 820
Nur Zincir-Heywood is an academic researcher from Dalhousie University. The author has contributed to research in topics: Computer science & Insider threat. The author has an hindex of 13, co-authored 61 publications receiving 640 citations.
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Journal Article
World wide web site summarization
TL;DR: In this article, an approach which applies machine learning and natural language processing techniques is developed to summarize a Web site automatically, and the information content of the automatically generated summaries is compared, via a formal evaluation process involving human subjects, to DMOZ summaries, home page browsing and time-limited site browsing, for a number of academic and commercial Web sites.
Proceedings ArticleDOI
Narrative text classification for automatic key phrase extraction in web document corpora
TL;DR: The evaluation shows that key phrases extracted from the narrative text only are significantly better than those obtained from all plain text of Web pages, demonstrating that narrative text classification is indispensable for effective key phrase extraction in Web document corpora.
Proceedings ArticleDOI
NetPal: a dynamic network administration knowledge base
Ashley George,Adetokunbo Makanju,Evangelos E. Milios,Nur Zincir-Heywood,Markus Latzel,Sotirios Stergiopoulos +5 more
TL;DR: The system architecture, user interface design, user software testing and future directions for development are described, including knowledge management, information retrieval, machine learning and network management.
Journal ArticleDOI
Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning
TL;DR: Evaluation results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders in unseen data with a high accuracy.
Proceedings ArticleDOI
On evolving buffer overflow attacks using genetic programming
TL;DR: This work employed genetic programming to evolve a "white hat" attacker; that is to say, variants of an attack are evolved with the objective of providing better detectors and evading detection by signature-based methods.