The Role of Machine Learning in Cybersecurity
- Journal
- arXiv (Cornell University)
In The Last Decade
doi.org/10.1145/3545574 →Countries where authors are citing The Role of Machine Learning in Cybersecurity
This map shows the geographic impact of The Role of Machine Learning in Cybersecurity. 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 The Role of Machine Learning in Cybersecurity with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The Role of Machine Learning in Cybersecurity more than expected).
Fields of papers citing The Role of Machine Learning in Cybersecurity
This network shows the impact of The Role of Machine Learning in Cybersecurity. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The Role of Machine Learning in Cybersecurity.
About The Role of Machine Learning in Cybersecurity
This paper, published in 2022, received 119 indexed citations . Written by Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de, Wissam Mallouli and Fabio Di Franco covering the research area of Signal Processing, Information Systems and Computer Networks and Communications. It is primarily cited by scholars working on Computer Networks and Communications (84 citations), Signal Processing (59 citations) and Artificial Intelligence (56 citations). Published in arXiv (Cornell University).
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/10.1145/3545574.