Public vulnerability databases, such as the National Vulnerability Database (NVD), document vulnerabilities and facilitate threat information sharing. However, they often suffer from short descriptions and outdated or insufficient information. In this paper, we introduceZad, a system designed to enrich NVD vulnerability descriptions by leveraging external resources.Zadconsists of two pipelines: one collects and filters supplementary data using two encoders to build a detailed dataset, while the other fine-tunes a pre-trained model on this dataset to generate enriched descriptions. By addressing brevity and improving content quality,Zadproduces more comprehensive and cohesive vulnerability descriptions. We evaluateZadusing standard summarization metrics and human assessments, demonstrating its effectiveness in enhancing vulnerability information.

This work proposes a multi-task Natural Language Processing (NLP) system to normalize and summarize the descriptions into a uniform structure. A dataset was curated from an official public database and broken into several constituent entities representing a particular aspect of the description. A model is trained on the annotated features independently and jointly to generate a simple and uniform summary. We also introduce our human metrics to judge the quality of the generated summary with respect to human comprehension and content accuracy.

The recent transformation of healthcare medical records from paper-based to digital and connected systems raises concerns regarding patients' security and online privacy. For instance, sensitive personal information, such as patients' names, addresses, and social security numbers, may be targeted due to the lack of proper security and privacy mechanisms. Using a total of 4,774 hospitals categorized as government, non-profit, and proprietary hospitals, this study provides the first measurement-based analysis of hospitals' websites and connects the findings with data breaches through a correlation analysis. We study the security attributes of three categories, collectively and in contrast, against domain name-, content-, and SSL certificate-level features. We find that each type of hospitals has a distinctive characteristic of its utilization of domain name registrars, top-level domain distribution, and domain creation distribution, as well as content type and HTTP request features. Security-wise, and consistent with the general population of websites, only 1% of government hospitals utilized DNSSEC, in contrast to 6% of the proprietary hospitals. Alarmingly, we found that 25% of the hospitals used plain HTTP, in contrast to 20% in the general web population. Alarmingly too, we found that 8%-84% of the hospitals, depending on their type, had some malicious contents, which are mostly attributed to the lack of maintenance. We conclude with a correlation analysis against 414 confirmed and manually vetted hospitals' data breaches. Among other interesting findings, our study highlights that the security attributes highlighted in our analysis of hospital websites are forming a very strong indicator of their likelihood of being breached. Our analyses are the first step towards understanding patient online privacy, highlighting the lack of basic security in many hospitals' websites and opening various potential research directions.