Automated Extraction of Vulnerability Information for Home Computer Security

Weerawardhana, Sachini; Mukherjee, Subhojeet; Ray, Indrajit; Howe, Adele E.

doi:10.1007/978-3-319-17040-4_24

Cited by 25 publications

(25 citation statements)

References 10 publications

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“…Mittal et al [1] analyze tweets about cybersecurity and issue timely threat alerts to security analysts. Weerawardhana et al [27] present machine learning-based and part-of-speech tagging approaches to information extraction from online vulnerability databases. Bridges et al [22] propose a Maximum Entropy Model trained with the many security corpus and achieve a high performance of identification and classification of appropriate entities.…”

Section: B Machine Learning-based Entity Extraction Methodsmentioning

confidence: 99%

Cybersecurity Named Entity Recognition Using Multi-Modal Ensemble Learning

et al. 2020

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Cybersecurity named entity recognition is an important part of threat information extraction from large-scale unstructured text collection in many cybersecurity applications. Most existing security entity recognition studies and systems use regular matching strategy or machine learning algorithms. Due to the peculiarity and complexity of security named entity, these models ignore the characteristic of security data and the correlation of entities. Therefore, through the in-depth study of security entity characteristic, we propose a novel security named entity recognition model based on regular expressions and known-entity dictionary as well as conditional random fields (CRF) combined with four feature templates. This model is named RDF-CRF. The rule-based expressions can match security entities with good accuracy in simpler situations, the known-entity dictionary can extract common and specific security entity, and the CRF-based extractor leverages the identified entities by rule-based and dictionary-based extractors to further improve the recognition performance. In order to demonstrate the effectiveness of our proposed model, extensive experiments are performed on a security text dataset collected from public security webs. The experimental results show that can achieve better performance than state-of-the-art methods.

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Section: B Machine Learning-based Entity Extraction Methodsmentioning

confidence: 99%

Cybersecurity Named Entity Recognition Using Multi-Modal Ensemble Learning

et al. 2020

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“…Few documents from the last three years can be found on the subject of NER in information security. A small number of works have focused on extracting vulnerabilities and attack information from unstructured texts in the past few years [11][12][13][14]. Bridges et al proposed a maximum entropy model trained with an averaged perceptron to extract entities from text, but these authors extracted only the entities and did not classify the types of these entities [11].…”

Section: Related Workmentioning

confidence: 99%

“…Bridges et al proposed a maximum entropy model trained with an averaged perceptron to extract entities from text, but these authors extracted only the entities and did not classify the types of these entities [11]. Weerawardhana et al extracted vulnerability information from an online vulnerability database [12]. Lal proposed a CRF to extract vulnerabilities from the text [13].…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, the development of NER tasks in information security has been relatively slow. In previous works, several methods have been proposed for extracting vulnerabilities and extracting information from unstructured texts [11][12][13][14]. In the past two years, research into NER has basically entered a stagnant state in the domain of information security.…”

Section: Introductionmentioning

confidence: 99%

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Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning

Zhang

Guo

2019

Security and Communication Networks

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In order to obtain high quality and large-scale labelled data for information security research, we propose a new approach that combines a generative adversarial network with the BiLSTM-Attention-CRF model to obtain labelled data from crowd annotations. We use the generative adversarial network to find common features in crowd annotations and then consider them in conjunction with the domain dictionary feature and sentence dependency feature as additional features to be introduced into the BiLSTM-Attention-CRF model, which is then used to carry out named entity recognition in crowdsourcing. Finally, we create a dataset to evaluate our models using information security data. The experimental results show that our model has better performance than the other baseline models.

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“…Mittal et al [1] analyzed tweets related to cybersecurity and issued timely threat alerts to security analysts. Weerawardhana et al [2] proposed a model that extracts information from online vulnerability databases, such as the Common Vulnerabilities and Exposures (CVE) list and the National Vulnerability Database. However, these resources focus on data from just one source.…”

Section: Introductionmentioning

confidence: 99%

Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields

Jiang

Lü

et al. 2021

Tinshhua Sci. Technol.

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Network texts have become important carriers of cybersecurity information on the Internet. These texts include the latest security events such as vulnerability exploitations, attack discoveries, advanced persistent threats, and so on. Extracting cybersecurity entities from these unstructured texts is a critical and fundamental task in many cybersecurity applications. However, most Named Entity Recognition (NER) models are suitable only for general fields, and there has been little research focusing on cybersecurity entity extraction in the security domain. To this end, in this paper, we propose a novel cybersecurity entity identification model based on Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM with CRF) to extract security-related concepts and entities from unstructured text. This model, which we have named XBiLSTM-CRF, consists of a word-embedding layer, a bidirectional LSTM layer, and a CRF layer, and concatenates X input with bidirectional LSTM output. Via extensive experiments on an open-source dataset containing an office security bulletin, security blogs, and the Common Vulnerabilities and Exposures list, we demonstrate that XBiLSTM-CRF achieves better cybersecurity entity extraction than state-of-the-art models.

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Automated Extraction of Vulnerability Information for Home Computer Security

Cited by 25 publications

References 10 publications

Cybersecurity Named Entity Recognition Using Multi-Modal Ensemble Learning

Cybersecurity Named Entity Recognition Using Multi-Modal Ensemble Learning

Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning

Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields

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