vepRisk - A Web Based Analysis Tool for Public Security Data

Andongabo, Ambrose; Gashi, Ilir

doi:10.1109/edcc.2017.30

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The objective of this study is to compare some aspects of the information present on vulnerability databases with another OSINT source, namely Twitter. Instead of searching, collecting, and parsing a set of databases, we use the vepRisk database [26]. It contains several types of security-related public data, including all entries published on the NVD, Security Database, Security Focus, and PacketStorm databases, from their creation until the end of 2018.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we present an extensive study on OSINT sources, comparing their timeliness and richness. We analysed the vulnerability OSINT sources indexed on vepRisk [26], which aggregates several vulnerability databases, advisory sites, and their relationships. We compared Twitter against these data sources to understand if there are any advantages in using it as a cybersecurity data source.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Follow the Blue Bird: A Study on Threat Data Published on Twitter

Alves

Andongabo

Gashi

et al. 2020

Computer Security – ESORICS 2020

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Open Source Intelligence (OSINT) has taken the interest of cybersecurity practitioners due to its completeness and timeliness. In particular, Twitter has proven to be a discussion hub regarding the latest vulnerabilities and exploits. In this paper, we present a study comparing vulnerability databases between themselves and against Twitter. Although there is evidence of OSINT advantages, no methodological studies have addressed the quality and benefits of the sources available. We compare the publishing dates of more than nine-thousand vulnerabilities in the sources considered. We show that NVD is not the most timely or the most complete vulnerability database, that Twitter provides timely and impactful security alerts, that using diverse OSINT sources provides better completeness and timeliness of vulnerabilities, and provide insights on how to capture cybersecurity-relevant tweets.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Follow the Blue Bird: A Study on Threat Data Published on Twitter

Alves

Andongabo

Gashi

et al. 2020

Computer Security – ESORICS 2020

Self Cite

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“…To obtain exploited vulnerability data, we used Exploit Database (EDB) 2 . The EDB is a CVE compliant archive of public exploits and corresponding vulnerable software, developed for use by penetration testers and vulnerability researchers [44].…”

Section: Dataset Usedmentioning

confidence: 99%

Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities

Movahedi

Cukier

Gashi

2021

IEEE Trans. Dependable and Secure Comput.

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Vulnerabilities with publically known exploits typically form 2-7% of all vulnerabilities reported for a given software version. With a smaller number of known exploited vulnerabilities compared with the total number of vulnerabilities, it is more difficult to model and predict when a vulnerability with a known exploit will be reported. In this paper, we introduce an approach for predicting the discovery pattern of publically known exploited vulnerabilities using all publically known vulnerabilities reported for a given software. Eight commonly used vulnerability discovery models (VDMs) and one neural network model (NNM) were utilized to evaluate the prediction capability of our approach. We compared their predictions results with the scenario when only exploited vulnerabilities were used for prediction. Our results show that, in terms of prediction accuracy, out of eight software we analyzed, our approach led to more accurate results in seven cases. Only in one case, the accuracy of our approach was worse by 1.6%.

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“…1 We then updated the reporting date on our database to the earliest date that a given vulnerability was known in any of these databases. Details of the tool (vepRisk) we developed to gather the data is given in [23]. For the rest of the paper, we will focus on four OSs (Windows, Mac, IOS, and Linux) and four web browsers (Internet Explorer, Safari, Firefox, and Chrome).…”

Section: Dataset and Data Processingmentioning

confidence: 99%

Cluster-based vulnerability assessment of operating systems and web browsers

et al. 2018

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Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) and web browsers they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process in modeling. We applied our approach to the vulnerabilities of four OSs (Windows, Mac, IOS, and Linux) and four web browsers (Internet Explorer, Safari, Firefox, and Chrome). Out of the total eight OSs and web browsers we analyzed using a power-law model issued from a family of SRMs, the model was statistically adequate for modeling in six cases. For these cases, in terms of estimation and forecasting capability, our results, compared to a power-law model without clustering, are more accurate in all cases but one.

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vepRisk - A Web Based Analysis Tool for Public Security Data

Cited by 3 publications

References 8 publications

Follow the Blue Bird: A Study on Threat Data Published on Twitter

Follow the Blue Bird: A Study on Threat Data Published on Twitter

Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities

Cluster-based vulnerability assessment of operating systems and web browsers

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