Ethan Bayne
Impact in
- Signal Processing top 5%
- Advanced Malware Detection Techniques
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- Network Security and Intrusion Detection
Papers in
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- Advanced Malware Detection Techniques 8
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- Network Security and Intrusion Detection 7
- Co-authors
- Hanan HindyRobert AtkinsonXavier BellekensChristos TachtatzisAmar SeeamDavid BrossetJean-Noël ColinIan Ferguson
- Journals
- Electronics (1 paper)Sensors (1 paper)IEEE Access (1 paper)Digital Investigation (1 paper)Middlesex University Research Repository (Middlesex University Of London) (1 paper)
- Partner nations
- United KingdomBelgiumFrance
In The Last Decade
Ethan Bayne
9 papers receiving 367 citations
Peers
Comparison fields: 5 of 30
- Signal Processing 230
- Computer Networks and Communications 343
- Artificial Intelligence 265
- Information Systems 77
- Hardware and Architecture 13
Countries citing papers authored by Ethan Bayne
This map shows the geographic impact of Ethan Bayne's research. 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 Ethan Bayne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ethan Bayne more than expected).
Fields of papers citing papers by Ethan Bayne
This network shows the impact of papers produced by Ethan Bayne. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ethan Bayne. The network helps show where Ethan Bayne may publish in the future.
Co-authors
The 10 scholars most cited alongside Ethan Bayne, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 2 | |
| 2 | 2021 | 10 | |
| 3 | Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study. | 2020 | 15 |
| 4 | 2020 | 153 | |
| 5 | 2020 | 5 | |
| 6 | 2020 | 126 | |
| 7 | 2018 | 8 | |
| 8 | 2018 | 70 | |
| 9 | Using multiple GPUs to accelerate string searching for digital forensic analysis | 2016 | 1 |
About Ethan Bayne
Ethan Bayne is a scholar working on Signal Processing, Computer Networks and Communications, Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 9 papers that have together received 390 indexed citations. Recurring topics across this work include Advanced Malware Detection Techniques (8 papers), Network Security and Intrusion Detection (7 papers), Internet Traffic Analysis and Secure E-voting (4 papers), Anomaly Detection Techniques and Applications (3 papers), Digital and Cyber Forensics (2 papers), Digital Media Forensic Detection (2 papers) and Image Processing and 3D Reconstruction (1 paper). The work is most often cited by research in Signal Processing (230 citations), Computer Networks and Communications (343 citations), Artificial Intelligence (265 citations), Information Systems (77 citations) and Hardware and Architecture (13 citations). Ethan Bayne has collaborated with scholars based in United Kingdom, Belgium and France. Frequent co-authors include Hanan Hindy, Robert Atkinson, Xavier Bellekens, Christos Tachtatzis, Amar Seeam, David Brosset, Jean-Noël Colin, Ian Ferguson, Miroslav Bureš and John P. Isaacs. Their work appears in journals such as Electronics, Sensors, IEEE Access, Digital Investigation and Middlesex University Research Repository (Middlesex University Of London).
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.