2019
DOI: 10.3390/app9193945
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Information Extraction of Cybersecurity Concepts: An LSTM Approach

Abstract: Extracting cybersecurity entities and the relationships between them from online textual resources such as articles, bulletins, and blogs and converting these resources into more structured and formal representations has important applications in cybersecurity research and is valuable for professional practitioners. Previous works to accomplish this task were mainly based on utilizing feature-based models. Feature-based models are time-consuming and need labor-intensive feature engineering to describe the prop… Show more

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Cited by 39 publications
(21 citation statements)
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“…Manual analysis of security vulnerabilities demands high expertise and imposes a huge burden on security analysts, which inevitably limits productivity. Thus, it is extremely useful to automate this process and recommend potentially vulnerable source files to security experts and developers with a given vulnerability description [4]. Hence, POS tagging is one of the early phases to be automated.…”
Section: A Background Of Security Vulnerabilitiesmentioning
confidence: 99%
See 3 more Smart Citations
“…Manual analysis of security vulnerabilities demands high expertise and imposes a huge burden on security analysts, which inevitably limits productivity. Thus, it is extremely useful to automate this process and recommend potentially vulnerable source files to security experts and developers with a given vulnerability description [4]. Hence, POS tagging is one of the early phases to be automated.…”
Section: A Background Of Security Vulnerabilitiesmentioning
confidence: 99%
“…Moreover, decimal numbers "3.0" and "6.5" that respectively follow a product "V 2I Hub" and "W ebAccess" provide critical information about which versions of a product are vulnerable to SQL injection or XSS vulnerabilities. The NLP approaches identify "3.0" and "6.5" as cardinal numbers; however, decimal numbers that follow a software product are expected to be versions of that product and not quantities of it [4]. Hence, in the security vulnerability domain, they are identified as proper nouns.…”
Section: B Motivation By Examplementioning
confidence: 99%
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“…Simran et al proposed a deep-learning-based framework for NER in cybersecurity and evaluated various deep-learning architectures [15]. Gasmi et al proposed an LSTM model for NER and Relation Extraction tasks [16]. Even though, not a deep learning approach Yi et al also proposed cyber-security NER model based on regular expressions and known-entity dictionary [17].…”
Section: Cyber-security Named Entity Recognitionmentioning
confidence: 99%