No abstract

No abstract

No abstract

The COVID-19 pandemic has caused many countries around the globe to put strict policies and measures in place in an attempt to control the rapid spread of the virus. These measures have affected economic activities and have impacted a broad range of businesses, such as international traveling, restaurants, and shopping malls. As COVID-19 vaccination efforts progress, countries are starting to relax international travel constraints and permit passengers from certain destinations to cross the border. Moreover, travelers from those destinations are likely required to provide certificates of vaccination results or negative COVID-19 tests before crossing the borders. Implementing these travel guidelines requires sharing information between countries, such as the number of COVID-19 cases and vaccination certificates for travelers. In this paper, we introduce SPIN, a framework leveraging a permissioned blockchain for sharing COVID-19 information between countries. This includes public data, such as the number of vaccinated people, and private data, such as vaccination certificates for individuals. Additionally, we employ cancelable fingerprint templates to authenticate private information about travelers. We analyze the framework from scalability, efficiency, security, and privacy perspectives. To validate our framework, we provide a prototype implementation using the Hyperledger Fabric platform.

Software vulnerabilities can expose computer systems to attacks by malicious actors. With the number of vulnerabilities discovered in the recent years surging, creating timely patches for every vulnerability is not always feasible. At the same time, not every vulnerability will be exploited by attackers; hence, prioritizing vulnerabilities by assessing the likelihood they will be exploited has become an important research problem. Recent works used machine learning techniques to predict exploited vulnerabilities by analyzing discussions about vulnerabilities on social media. These methods relied on traditional text processing techniques, which represent statistical features of words, but fail to capture their context. To address this challenge, we propose DarkEmbed, a neural language modeling approach that learns low dimensional distributed representations, i.e., embeddings, of darkweb/deepweb discussions to predict whether vulnerabilities will be exploited. By capturing linguistic regularities of human language, such as syntactic, semantic similarity and logic analogy, the learned embeddings are better able to classify discussions about exploited vulnerabilities than traditional text analysis methods. Evaluations demonstrate the efficacy of learned embeddings on both structured text (such as security blog posts) and unstructured text (darkweb/deepweb posts). DarkEmbed outperforms state-of-the-art approaches on the exploit prediction task with an F1-score of 0.74.