Early Detection of Vulnerabilities from News Websites using Machine Learning Models

Iorga, Denis; Corlatescu, Dragos; Grigorescu, Octavian; Sandescu, Cristian; Dascălu, Mihai; Rughiniș, Răzvan

doi:10.1109/roedunet51892.2020.9324852

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Our aggregated corpus for training and evaluating our models consists of 1600 news articles from two collections of labeled articles that were obtained using two different approaches: Iorga et al (2020) introduce a corpus of 1000 news articles on cybersecurity manually labeled by experts from news outlets, and Iorga et al (2021) consider 600 more articles that were extracted from selected Tweeter accounts. The distribution in terms of length is displayed in Figure 1; as it can be observed, more than half of the articles exceed the usual length of 512 tokens acceptable by most pretrained language models.…”

Section: Corporamentioning

confidence: 99%

Interpretable Identification of Cybersecurity Vulnerabilities from News Articles

Foret¹,

Ruşeţi

Sandescu

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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Section: Corporamentioning

confidence: 99%

Interpretable Identification of Cybersecurity Vulnerabilities from News Articles

Foret¹,

Ruşeţi

Sandescu

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

Self Cite

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“…Supervised classification has been used on tweets related to IT assets [4], tweets containing at least one security keyword from certain user accounts [2], tweets from cyber experts with n-grams extracted [13], and social media threats from multiple platforms [10]. [8].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Unsupervised Classificiation and Data Mining of Tweets about Cyber Vulnerabilities

Alperin,

Joback,

Shing

et al. 2021

Preprint

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Many cyber network defense tools rely on the National Vulnerability Database (NVD) to provide timely information on known vulnerabilities that exist within systems on a given network. However, recent studies have indicated that the NVD is not always up to date, with known vulnerabilities being discussed publicly on social media platforms, like Twitter and Reddit, months before they are published to the NVD. To that end, we present a framework for unsupervised classification to filter tweets for relevance to cyber security. We consider and evaluate two unsupervised machine learning techniques for inclusion in our framework, and show that zero-shot classification using a Bidirectional and Auto-Regressive Transformers (BART) model outperforms the other technique with 83.52% accuracy and a F1 score of 83.88, allowing for accurate filtering of tweets without human intervention or labelled data for training. Additionally, we discuss different insights that can be derived from these cyber-relevant tweets, such as trending topics of tweets and the counts of Twitter mentions for Common Vulnerabilities and Exposures (CVEs), that can be used in an alert or report to augment current NVD-based risk assessment tools.

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“…considers using ML in predicting ever, the context of our work is not specific to SMEs, hence we focus the broader healthcare system with CVE database. There is another work[45] that illustrates using social media, news articles and open-source data to predict vulnerabilities in cybersecurity, using two ML models: Vector Machines and fine-tuned BERT. The result indicates that the model BERT performs better than Vector Machine.…”

mentioning

confidence: 99%

Vulnerability prediction for secure healthcare supply chain service delivery

Islam

Abba

Ismail

et al. 2022

ICA

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Healthcare organisations are constantly facing sophisticated cyberattacks due to the sensitivity and criticality of patient health care information and wide connectivity of medical devices. Such attacks can pose potential disruptions to critical services delivery. There are number of existing works that focus on using Machine Learning (ML) models for predicting vulnerability and exploitation but most of these works focused on parameterized values to predict severity and exploitability. This paper proposes a novel method that uses ontology axioms to define essential concepts related to the overall healthcare ecosystem and to ensure semantic consistency checking among such concepts. The application of ontology enables the formal specification and description of healthcare ecosystem and the key elements used in vulnerability assessment as a set of concepts. Such specification also strengthens the relationships that exist between healthcare-based and vulnerability assessment concepts, in addition to semantic definition and reasoning of the concepts. Our work also makes use of Machine Learning techniques to predict possible security vulnerabilities in health care supply chain services. The paper demonstrates the applicability of our work by using vulnerability datasets to predict the exploitation. The results show that the conceptualization of healthcare sector cybersecurity using an ontological approach provides mechanisms to better understand the correlation between the healthcare sector and the security domain, while the ML algorithms increase the accuracy of the vulnerability exploitability prediction. Our result shows that using Linear Regression, Decision Tree and Random Forest provided a reasonable result for predicting vulnerability exploitability.

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Early Detection of Vulnerabilities from News Websites using Machine Learning Models

Cited by 9 publications

References 15 publications

Interpretable Identification of Cybersecurity Vulnerabilities from News Articles

Interpretable Identification of Cybersecurity Vulnerabilities from News Articles

A Framework for Unsupervised Classificiation and Data Mining of Tweets about Cyber Vulnerabilities

Vulnerability prediction for secure healthcare supply chain service delivery

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