Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media

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

doi:10.48550/arxiv.1902.10680

Cited by 5 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rodriguez et al use keywords to filter for cyber-relevance and active learning to update the keywords, and use tweet sentiment as a feature over time [15]. Zong et al study the connection of severity of cyber threats with how they are discussed online [22]. Hoppa et al visualize cyber-relevant tweets in Kibana for end users [6].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The progression of cybercrime and the development and adoption of new techniques to jeopardize sensitive information and impart damage across the Internet present an alarming threat to businesses, governments, and nations. Recent cybersecurity research (e.g., the works in [1][2][3][4][5][6]) confirms cybercriminals' determination to develop newer techniques for achieving their malicious objectives. Ransomware is just one of the methods that have been used recently by cybercriminals to achieve financial gains in return for releasing ransomware-encrypted files to their rightful owners.…”

Section: Introductionmentioning

confidence: 97%

Ransomware-Resilient Self-Healing XML Documents

et al. 2022

View full text Add to dashboard Cite

In recent years, various platforms have witnessed an unprecedented increase in the number of ransomware attacks targeting hospitals, governments, enterprises, and end-users. The purpose of this is to maliciously encrypt documents and files on infected machines, depriving victims of access to their data, whereupon attackers would seek some sort of a ransom in return for restoring access to the legitimate owners; hence the name. This cybersecurity threat would inherently cause substantial financial losses and time wastage for affected organizations and users. A great deal of research has taken place across academia and around the industry to combat this threat and mitigate its danger. These ongoing endeavors have resulted in several detection and prevention schemas. Nonetheless, these approaches do not cover all possible risks of losing data. In this paper, we address this facet and provide an efficient solution that would ensure an efficient recovery of XML documents from ransomware attacks. This paper proposes a self-healing version-aware ransomware recovery (SH-VARR) framework for XML documents. The proposed framework is based on the novel idea of using the link concept to maintain file versions in a distributed manner while applying access-control mechanisms to protect these versions from being encrypted or deleted. The proposed SH-VARR framework is experimentally evaluated in terms of storage overhead, time requirement, CPU utilization, and memory usage. Results show that the snapshot size increases proportionately with the original size; the time required is less than 120 ms for files that are less than 1 MB in size; and the highest CPU utilization occurs when using the bzip2. Moreover, when the zip and gzip are used, the memory usage is almost fixed (around 6.8 KBs). In contrast, it increases to around 28 KBs when the bzip2 is used.

show abstract

“…Fan Shi [18] made use of XLNet model and self-built corpus for fine tuning to predict measures from the vulnerability description text, thus reducing the burden of the evaluation process. Zong et al [19] have studied network security threat contents on social media, trying to predict the severity of vulnerabilities based on these contents. In the study of Zou D et al [20], it is believed that the severity of vulnerability is determined according to the attack process of vulnerability.…”

Section: Introductionmentioning

confidence: 99%

Research on intelligent recognition method of software vulnerability severity based on event extraction

Wen

Lin

et al. 2023

Preprint

View full text Add to dashboard Cite

The number of vulnerability reports is rapidly increasing, and relying on human resources for vulnerability analysis will become more difficult. In addition, the current vulnerability report research is mainly based on general vulnerability disclosure (CVE) or other foreign open source projects, and there are few researches on Chinese vulnerability reports. With the development of deep learning, the research on event extraction has also been greatly developed. In order to contribute to the vulnerability analysis of Chinese vulnerability report, this paper adopts the method of event extraction to automatically mine the vulnerability report of the National Information security Vulnerability Sharing platform (CNVD). A new vulnerability analysis method is proposed, that is, the severity level of vulnerability report is evaluated by the vulnerability description information and the event information. At the same time, a convolutional neural network method named EICNN is proposed to realize this new vulnerability analysis method by integrating event information. An application that obtains the vulnerability reporting event information and evaluates the vulnerability severity at the end.

show abstract

Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media

Cited by 5 publications

References 21 publications

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

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

Ransomware-Resilient Self-Healing XML Documents

Research on intelligent recognition method of software vulnerability severity based on event extraction

Contact Info

Product

Resources

About