Ivan Evtimov
Impact in
- Artificial Intelligence top 1%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Signal Processing top 2%
- Advanced Malware Detection Techniques
Papers in
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- Adversarial Robustness in Machine Learning 2
- Privacy-Preserving Technologies in Data 1
- Internet Traffic Analysis and Secure E-voting 1
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- Network Security and Intrusion Detection 1
- Co-authors
- Tadayoshi Kohno (4 shared papers)Earlence Fernandes (3 shared papers)Kevin Eykholt (2 shared papers)Atul Prakash (2 shared papers)Amir Rahmati (2 shared papers)Chaowei Xiao (1 shared paper)Dawn Song (1 shared paper)Bo Li (1 shared paper)
- Journals
- SHILAP Revista de lepidopterología (1 paper)Berkeley technology law journal (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Ivan Evtimov
5 papers receiving 982 citations
Ivan Evtimov's Hit Papers
Peers
Comparison fields: 5 of 86
- Artificial Intelligence 853
- Signal Processing 263
- Hardware and Architecture 104
- Health Informatics 16
- Computer Vision and Pattern Recognition 205
Countries citing papers authored by Ivan Evtimov
This map shows the geographic impact of Ivan Evtimov'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 Ivan Evtimov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Evtimov more than expected).
Fields of papers citing papers by Ivan Evtimov
This network shows the impact of papers produced by Ivan Evtimov. 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 Ivan Evtimov. The network helps show where Ivan Evtimov may publish in the future.
Co-authors
The 16 scholars most cited alongside Ivan Evtimov, 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 | Robust Physical-World Attacks on Deep Learning Visual Classification Hit paper breakdown → | 2018 | 993 |
| 2 | 2018 | 7 | |
| 3 | 2021 | 6 | |
| 4 | Tools for Active and Passive Network Side-Channel Detection for Web Applications | 2018 | 5 |
| 5 | 2019 | 1 |
About Ivan Evtimov
Ivan Evtimov is a scholar working on Artificial Intelligence, Computer Networks and Communications, Molecular Biology, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 5 papers that have together received 1.0k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Privacy-Preserving Technologies in Data (1 paper), Advanced Malware Detection Techniques (1 paper), Bacillus and Francisella bacterial research (1 paper), Face recognition and analysis (1 paper), Network Security and Intrusion Detection (1 paper), Integrated Circuits and Semiconductor Failure Analysis (1 paper) and Internet Traffic Analysis and Secure E-voting (1 paper). The work is most often cited by research in Artificial Intelligence (853 citations), Signal Processing (263 citations), Hardware and Architecture (104 citations), Health Informatics (16 citations) and Computer Vision and Pattern Recognition (205 citations). Ivan Evtimov has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Tadayoshi Kohno, Earlence Fernandes, Kevin Eykholt, Atul Prakash, Amir Rahmati, Chaowei Xiao, Dawn Song, Bo Li, Pascal Sturmfels and Ryan Calo. Their work appears in journals such as SHILAP Revista de lepidopterología, Berkeley technology law journal and SSRN Electronic Journal.
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.