Meltdown: reading kernel memory from user space
Impact in
Classified as
- Journal
- USENIX Security Symposium
In The Last Decade
doi.org/w3218892 →Countries where authors are citing Meltdown: reading kernel memory from user space
This map shows the geographic impact of Meltdown: reading kernel memory from user space. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Meltdown: reading kernel memory from user space with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Meltdown: reading kernel memory from user space more than expected).
Fields of papers citing Meltdown: reading kernel memory from user space
This network shows the impact of Meltdown: reading kernel memory from user space. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Meltdown: reading kernel memory from user space.
About Meltdown: reading kernel memory from user space
This paper, published in 2018, received 388 indexed citations . Written by Moritz Lipp, Michael Schwarz, Daniel Gruss, Thomas Prescher, Werner Haas, Anders Fogh, Stefan Mangard, Paul Kocher, Daniel Genkin and Yuval Yarom covering the research area of Hardware and Architecture, Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (326 citations), Signal Processing (181 citations), Hardware and Architecture (135 citations), Computer Networks and Communications (116 citations) and Information Systems (113 citations). Published in USENIX Security Symposium.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w3218892.