Domain-Oriented Topic Discovery Based on Features Extraction and Topic Clustering

• Published in: IEEE access Vol. 8; pp. 93648 - 93662
• Main Authors: Lu, Xiaofeng, Zhou, Xiao, Wang, Wenting, Lio, Pietro, Hui, Pan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; Clustering algorithms; Computer security; Cybersecurity; Data mining; Data models; Domains; Feature extraction; Indexing; Malware; threat intelligence; topic clustering; Topic detection; transfer learning; Twitter; Webs
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2994516

Topic detection technology can automatically discover new topics on the Internet. This paper investigates domain-oriented feature extraction methods, and proposes a keyword feature extraction method ITFIDF-LP, a subject word feature extraction method LDA-SLP and a topic clustering model based on vector product similarity. A novel Domain-oriented Topic Discovery based on Features Extraction and Topic Clustering (DTD-FETC) model is proposed to analyze open source web of a domain and identify emerging topics in the domain in real time. This article describes a DTD-FETC system built for cyber security domain. It filters and aggregates web for specical security threat topics such as vulnerability and malware, and helps security staff respond quickly and defends against the emerging cyber threats as early as possible. The recall rate, accuracy and F1 value results of the DTD-FETC method applied to the cyber security dataset are all above 0.99.