Simon Koch, David Klein, and Martin Johns (TU Braunschweig)

Are GitHub stars a good surrogate metric to assess the importance of open-source code? While security research frequently uses them as a proxy for importance, the reliability of this relationship has not been studied yet. Furthermore, its relationship to download numbers provided by code registries – another commonly used metric – has yet to be ascertained. We address this research gap by analyzing the correlation between both GitHub stars and download numbers as well as their correlation with detected deployments across websites. Our data set consists of 925 978 data points across three web programming languages: PHP, Ruby, and JavaScript. We assess deployment across websites using 58 hand-crafted fingerprints for JavaScript libraries. Our results reveal a weak relationship between GitHub Stars and download numbers ranging from a correlation of 0.47 for PHP down to 0.14 for JavaScript, as well as a high amount of low star and high download projects for PHP and Ruby and an opposite pattern for JavaScript with a noticeably higher count of high star and apparently low download libraries. Concerning the relationship for detected deployments, we discovered a correlation of 0.61 and 0.63 with stars and downloads, respectively. Our results indicate that both downloads and stars pose a moderately strong indicator of the importance of client-side deployed JavaScript libraries.

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Front-running Attack in Sharded Blockchains and Fair Cross-shard Consensus

Jianting Zhang (Purdue University), Wuhui Chen (Sun Yat-sen University), Sifu Luo (Sun Yat-sen University), Tiantian Gong (Purdue University), Zicong Hong (The Hong Kong Polytechnic University), Aniket Kate (Purdue University)

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Reverse Engineering of Multiplexed CAN Frames (Long)

Alessio Buscemi, Thomas Engel (SnT, University of Luxembourg), Kang G. Shin (The University of Michigan)

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Chhoyhopper: A Moving Target Defense with IPv6

A S M Rizvi (University of Southern California/Information Sciences Institute) and John Heidemann (University of Southern California/Information Sciences Institute)

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Strengthening Privacy in Robust Federated Learning through Secure Aggregation

Tianyue Chu, Devriş İşler (IMDEA Networks Institute & Universidad Carlos III de Madrid), Nikolaos Laoutaris (IMDEA Networks Institute)

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