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Durham University

Research & business

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Publication details

Al Moubayed, N., Wall, D. & McGough, A. S. (2017). Identifying Changes in the Cybersecurity Threat Landscape using the LDA-Web Topic Modelling Data Search Engine. In Human aspects of information security, privacy and trust: 5th International Conference, HAS 2017, held as part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, proceedings. Tryfonas, Theo Cham: Springer. 287-295.

Author(s) from Durham


Successful Cybersecurity depends on the processing of vast quantities of data from a diverse range of sources such as police reports, blogs, intelligence reports, security bulletins, and news sources. This results in large volumes of unstructured text data that is difficult to manage or investigate manually. In this paper we introduce a tool that summarises, categorises and models such data sets along with a search engine to query the model produced from the data. The search engine can be used to find links, similarities and differences between different documents in a way beyond the current search approaches. The tool is based on the probabilistic topic modelling technique which goes further than the lexical analysis of documents to model the subtle relationships between words, documents, and abstract topics. It will assists researchers to query the underlying models latent in the documents and tap into the repository of documents allowing them o be ordered thematically.