From throw-away traffic to bots: detecting the rise of DGA-based malware
- Journal
- USENIX Security Symposium
In The Last Decade
doi.org/w4422011 →Countries where authors are citing From throw-away traffic to bots: detecting the rise of DGA-based malware
This map shows the geographic impact of From throw-away traffic to bots: detecting the rise of DGA-based malware. 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 From throw-away traffic to bots: detecting the rise of DGA-based malware with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites From throw-away traffic to bots: detecting the rise of DGA-based malware more than expected).
Fields of papers citing From throw-away traffic to bots: detecting the rise of DGA-based malware
This network shows the impact of From throw-away traffic to bots: detecting the rise of DGA-based malware. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the From throw-away traffic to bots: detecting the rise of DGA-based malware.
About From throw-away traffic to bots: detecting the rise of DGA-based malware
This paper, published in 2012, received 285 indexed citations . Written by Manos Antonakakis, Roberto Perdisci, Yacin Nadji, Nikolaos Vasiloglou, Saeed Abu‐Nimeh, Wenke Lee and David Dagon covering the research area of Signal Processing, Artificial Intelligence and Computer Networks and Communications. It is primarily cited by scholars working on Computer Networks and Communications (258 citations), Artificial Intelligence (193 citations) and Signal Processing (174 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/w4422011.