Security OSIF: Toward Automatic Discovery and Analysis of Event Based Cyber Threat Intelligence

Li, Ke; Wen, Hui; Li, Hong; Zhu, Hongsong; Sun, Limin

doi:10.1109/smartworld.2018.00142

Cited by 20 publications

(9 citation statements)

References 8 publications

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“…Li et al proposed the OSIF framework, which automatically analyzed open source data to generate a chain of threat intelligence events [5]. First, security event related articles were extracted from the open source platform according to the given event keywords and preprocessed.…”

Section: B Topic Detection and Tracking For Cyber Securitymentioning

confidence: 99%

“…In cyber security domain, security staff collect and track the latest security news or reports daily to help them improve security defense strategies [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, researchers have mainly used data mining and correlation analysis techniques to study threat intelligence [10]. Researchers automatically identify threat topics from fragmented, open source threat data, helping security analysts respond quickly and protect against emerging cyber threats [5].…”

Section: Introductionmentioning

confidence: 99%

“…Instead, security reports in cyber security technology blogs and web sites sometimes have a comprehensive description of the attack and are more suitable for security practitioners [5]. The descriptions in such web pages are usually informal descriptions of natural language and require careful analysis to restore relevant attack instructions.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we study security topic detection from open source web pages with high information relevance, such as security blogs and security newletters. Security reports or newsletters are produced at a high volume and velocity on the Internet [5].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Zhou

Wang³

et al. 2020

IEEE Access

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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.

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Section: B Topic Detection and Tracking For Cyber Securitymentioning

confidence: 99%

“…In cyber security domain, security staff collect and track the latest security news or reports daily to help them improve security defense strategies [4], [5].…”

Section: Introductionmentioning

confidence: 99%