Chen Hajaj

559 citations
44 papers · 296 · h-index 12

Impact in

Papers in

Chen Hajaj

38 papers receiving 288 citations

Peers

Chen Hajaj
Comparison fields: 5 of 75
  • Signal Processing 86
  • Management Science and Operations Research 63
  • Marketing 42
  • Computer Networks and Communications 90
  • General Decision Sciences 7
Replace Michael Benisch with:
Michael Benisch United States
Hugues Bouchard United States
Javier Parra‐Arnau Spain
Maliheh Izadi Netherlands
Lu Ning China
Sylvain Bouveret France
Bo Long United States
Ramón Hermoso Spain
Andrew Byde United Kingdom
Pengdeng Li China
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Citations per field
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Citations per year

Countries citing papers authored by Chen Hajaj

Since Specialization
Citations

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

Fields of papers citing papers by Chen Hajaj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Chen Hajaj, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Chen Hajaj Line = papers co-authored together Chen Hajaj links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202229
2 201520
3 202218
4
A Crowdsourcing Framework for Medical Data Sets.
201818
5 201318
6
Improving robustness of ML classifiers against realizable evasion attacks using conserved features
201916
7 202214
8 201614
9 202213
10 201513
11 202312
12 201412
13 202211
14 202010
15 20218
16 20148
17 20168
18
A Framework for Validating Models of Evasion Attacks on Machine Learning, with Application to PDF Malware Detection
20177
19 20216
20 20175

About Chen Hajaj

Chen Hajaj is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Management Science and Operations Research and Marketing, having authored 44 papers that have together received 296 indexed citations. Recurring topics across this work include Network Security and Intrusion Detection (16 papers), Advanced Malware Detection Techniques (12 papers), Auction Theory and Applications (9 papers), Internet Traffic Analysis and Secure E-voting (9 papers), Consumer Market Behavior and Pricing (8 papers), Digital Platforms and Economics (4 papers), Adversarial Robustness in Machine Learning (4 papers) and Mobile Crowdsensing and Crowdsourcing (4 papers). The work is most often cited by research in Signal Processing (86 citations), Management Science and Operations Research (63 citations), Marketing (42 citations), Computer Networks and Communications (90 citations) and General Decision Sciences (7 citations). Chen Hajaj has collaborated with scholars based in Israel, United States and France. Frequent co-authors include David Sarne, Amit Dvir, Noam Hazon, Anat Goldstein, Yevgeniy Vorobeychik, Tüomas Sandholm, Chaowei Xiao, Avinatan Hassidim, John P. Dickerson and Bo Li. Their work appears in journals such as Computers & Security, IEEE Access, Electronics, Expert Systems with Applications and Autonomous Agents and Multi-Agent Systems.

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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