vepRisk - A Web Based Analysis Tool for Public Security Data

We present vepRisk (Vulnerabilities, Exploits and Patches Risk analysis tool): a web-based tool for analyzing publically available security data. The tool has a backend modules that mine, extract, parse and store data from public repositories of vulnerabilities, exploits and patches; and a frontend web-based application that provides functionality for analyzing the data. The frontend uses shinyR, hence allowing integration with the R statistical analysis package and seamless use of R functions. We also present initial analysis we have done with the tool, and outline the extensions and future development we plan to integrate into the tool in the near future

Cluster-based Vulnerability Assessment of Operating Systems and Web Browsers

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 (NHPP) 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

Follow the blue bird: A study on threat data published on Twitter

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

Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities

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%

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity

Some Guidelines for Risk Assessment of Vulnerability Discovery Processes

Software vulnerabilities can be defined as software faults, which can be exploited as results of security attacks. Security researchers have used data from vulnerability databases to study trends of discovery of new vulnerabilities or propose models for fitting the discovery times and for predicting when new vulnerabilities may be discovered. Estimating the discovery times for new vulnerabilities is useful both for vendors as well as the end-users as it can help with resource allocation strategies over time. Among the research conducted on vulnerability modeling, only a few studies have tried to provide a guideline about which model should be used in a given situation. In other words, assuming the vulnerability data for a software is given, the research questions are the following: Is there any feature in the vulnerability data that could be used for identifying the most appropriate models for that dataset? What models are more accurate for vulnerability discovery process modeling? Can the total number of publicly-known exploited vulnerabilities be predicted using all vulnerabilities reported for a given software? To answer these questions, we propose to characterize the vulnerability discovery process using several common software reliability/vulnerability discovery models, also known as Software Reliability Models (SRMs)/Vulnerability Discovery Models (VDMs). We plan to consider different aspects of vulnerability modeling including curve fitting and prediction. Some existing SRMs/VDMs lack accuracy in the prediction phase. To remedy the situation, three strategies are considered: (1) Finding a new approach for analyzing vulnerability data using common models. In other words, we examine the effect of data manipulation techniques (i.e. clustering, grouping) on vulnerability data, and investigate whether it leads to more accurate predictions. (2) Developing a new model that has better curve filling and prediction capabilities than current models. (3) Developing a new method to predict the total number of publicly-known exploited vulnerabilities using all vulnerabilities reported for a given software. The dissertation is intended to contribute to the science of software reliability analysis and presents some guidelines for vulnerability risk assessment that could be integrated as part of security tools, such as Security Information and Event Management (SIEM) systems