Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media

Zong, Shi; Ritter, Alan; Mueller, Graham; Wright, Evan

doi:10.18653/v1/n19-1140

Cited by 29 publications

(7 citation statements)

References 22 publications

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“…The results showed that in their experiments, the NN-based algorithms provide less accuracy than DT-based algorithms. The authors of one paper [ 32 ] presented a method to analyze the severity of CS threats analyzing the language of CS-related tweets through a DL approach. The experiments used a corpus of 6000 tweets containing the description of software vulnerabilities, annotated with the opinions of the authors toward their severity.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

Silvestri

Islam

Tzagkarakis

et al. 2023

Sensors

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Digitization in healthcare systems, with the wid adoption of Electronic Health Records, connected medical devices, software and systems providing efficient healthcare service delivery and management. On the other hand, the use of these systems has significantly increased cyber threats in the healthcare sector. Vulnerabilities in the existing and legacy systems are one of the key causes for the threats and related risks. Understanding and addressing the threats from the connected medical devices and other parts of the ICT health infrastructure are of paramount importance for ensuring security within the overall healthcare ecosystem. Threat and vulnerability analysis provides an effective way to lower the impact of risks relating to the existing vulnerabilities. However, this is a challenging task due to the availability of massive data which makes it difficult to identify potential patterns of security issues. This paper contributes towards an effective threats and vulnerabilities analysis by adopting Machine Learning models, such as the BERT neural language model and XGBoost, to extract updated information from the Natural Language documents largely available on the web, evaluating at the same time the level of the identified threats and vulnerabilities that can impact on the healthcare system, providing the required information for the most appropriate management of the risk. Experiments were performed based on CS news extracted from the Hacker News website and on Common Vulnerabilities and Exposures (CVE) vulnerability reports. The results demonstrate the effectiveness of the proposed approach, which provides a realistic manner to assess the threats and vulnerabilities from Natural Language texts, allowing adopting it in real-world Healthcare ecosystems.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

Silvestri

Islam

Tzagkarakis

et al. 2023

Sensors

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“…Zong et al have collected tweets containing a keyword, "vulnerability", and have applied logistic regression to detect the existence of threats. They have also applied logistic regression and CNN to analyze the extent of threats and the user's opinions on social media about cyber threats [51].…”

Section: Classification By the Cybersecurity Intelligencementioning

confidence: 99%

A New Text Classification Model Based on Contrastive Word Embedding for Detecting Cybersecurity Intelligence in Twitter

2020

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Detecting cybersecurity intelligence (CSI) on social media such as Twitter is crucial because it allows security experts to respond cyber threats in advance. In this paper, we devise a new text classification model based on deep learning to classify CSI-positive and -negative tweets from a collection of tweets. For this, we propose a novel word embedding model, called contrastive word embedding, that enables to maximize the difference between base embedding models. First, we define CSI-positive and -negative corpora, which are used for constructing embedding models. Here, to supplement the imbalance of tweet data sets, we additionally employ the background knowledge for each tweet corpus: (1) CVE data set for CSI-positive corpus and (2) Wikitext data set for CSI-negative corpus. Second, we adopt the deep learning models such as CNN or LSTM to extract adequate feature vectors from the embedding models and integrate the feature vectors into one classifier. To validate the effectiveness of the proposed model, we compare our method with two baseline classification models: (1) a model based on a single embedding model constructed with CSI-positive corpus only and (2) another model with CSI-negative corpus only. As a result, we indicate that the proposed model shows high accuracy, i.e., 0.934 of F1-score and 0.935 of area under the curve (AUC), which improves the baseline models by 1.76∼6.74% of F1-score and by 1.64∼6.98% of AUC.

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“…Although cybersecurity has traditionally focused on data created within an organization, opportunities arise from analyzing UD generated outside the organization. For instance, the severity of vulnerabilities can be forecasted analyzing tweets [19]. Tweets can also be analyzed to extract topics, opinions, and knowledge related to security breaches from consumers.…”

Section: Cybersecurity and Unstructured Datamentioning

confidence: 99%

Unstructured Data for Cybersecurity and Internal Control

Canelón

Huerta

Leal³

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper proposes a research framework for studying the connections-realized and potential-between unstructured data (UD) and cybersecurity and internal controls. In the framework, cybersecurity and internal control goals determine the tasks to be conducted. The task influences the types of UD to be accessed and the types of analysis to be done, which in turn influences the outcomes that can be achieved. Patterns in UD are relevant for cybersecurity and internal control, but UD poses unique challenges for its analysis and management. This paper discusses some of these challenges including veracity, structuralizing, bias, and explainability.

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Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media

Cited by 29 publications

References 22 publications

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

A New Text Classification Model Based on Contrastive Word Embedding for Detecting Cybersecurity Intelligence in Twitter

Unstructured Data for Cybersecurity and Internal Control

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