Blaine Nelson

3.4k citations
15 papers · 1.1k indexed · 1 hit paper · h-index 9
Topics
Adversarial Robustness in Machine Learning (8 papers)Anomaly Detection Techniques and Applications (7 papers)Network Security and Intrusion Detection (7 papers)
Journals
NeurocomputingJournal of Machine Learning ResearchUNICA IRIS Institutional Research Information System (University of Cagliari)

In The Last Decade

Blaine Nelson

13 papers receiving 1.0k citations

Hit Papers

Adversarial machine learning20112026201620212011200400600

Peers

Blaine Nelson
Comparison fields: 5 of 80
  • Artificial Intelligence 880
  • Signal Processing 437
  • Computer Networks and Communications 361
  • Information Systems 209
  • Computer Vision and Pattern Recognition 126
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Citations per field
00.5×1.6×
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Citations per year

Countries citing papers authored by Blaine Nelson

Since Specialization
Citations

This map shows the geographic impact of Blaine Nelson'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 Blaine Nelson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Blaine Nelson more than expected).

Fields of papers citing papers by Blaine Nelson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Blaine Nelson. 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 Blaine Nelson. The network helps show where Blaine Nelson may publish in the future.

Co-authorship network of co-authors of Blaine Nelson

This figure shows the co-authorship network connecting the top 25 collaborators of Blaine Nelson. A scholar is included among the top collaborators of Blaine Nelson based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Blaine Nelson. Blaine Nelson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
#WorkIndexed citations
1 2
2 37
3 133
4 11
5 1
6 48
7
Support Vector Machines Under Adversarial Label Noise
158
8 11
9
Adversarial machine learningbreakdown →
648
10
Evading Anomaly Detection through Variance Injection Attacks on PCA (Extended Abstract)
2
11
Compromising PCA-based Anomaly Detectors for Network-Wide Traffic
18
12
Designing, Implementing, and Analyzing a System for Virus Detection
3
13
Analyzing Behavioral Features for Email Classification.
40
14 3
15 1

About Blaine Nelson

Blaine Nelson is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing, having authored 15 papers that have together received 1.1k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (8 papers), Anomaly Detection Techniques and Applications (7 papers) and Network Security and Intrusion Detection (7 papers). The work is most often cited by research in Signal Processing (437 citations), Artificial Intelligence (880 citations) and Computer Networks and Communications (361 citations). Blaine Nelson has collaborated with scholars based in United States, Germany and Italy. Frequent co-authors include Anthony D. Joseph, Benjamin I. P. Rubinstein, J. D. Tygar, Ling Huang, Battista Biggio, Pavel Laskov, Fabio Roli, Claudia Eckert, Xiao Huang and Han Xiao. Their work appears in journals such as Neurocomputing, Journal of Machine Learning Research and UNICA IRIS Institutional Research Information System (University of Cagliari).

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.

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