Mark Stamp

106 papers receiving 2.5k citations

Mark Stamp's Hit Papers

A comparison of static, dynamic, and hybrid analysis for malware detection 2015 · 300 citations
3000+3+7Years since publication100200300

Peers

Mark Stamp
Comparison fields: 5 of 108
  • Signal Processing 2.0k
  • Software 349
  • Computer Networks and Communications 1.8k
  • Information Systems 1.2k
  • Artificial Intelligence 1.1k
Replace Danfeng Yao with:
Danfeng Yao United States
Patrick Traynor United States
A. Nur Zincir‐Heywood Canada
Iulian Neamtiu United States
Guoai Xu China
Cristina Nita-Rotaru United States
Deqing Zou China
Yingjiu Li Singapore
Helen J. Wang United States
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Countries citing papers authored by Mark Stamp

Since Specialization
Citations

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

Fields of papers citing papers by Mark Stamp

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mark Stamp, 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 Mark Stamp Line = papers co-authored together Mark Stamp links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 111 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A comparison of static, dynamic, and hybrid analysis for malware detection
Hit paper breakdown →
2015300
2 2006168
3
Information Security: Principles and Practices
2005165
4 2012105
5 2013100
6 201083
7 200878
8 199378
9 201177
10 201475
11 201366
12 201265
13 201963
14 201961
15 201859
16 201254
17 201352
18 202049
19 201345
20 201744

About Mark Stamp

Mark Stamp is a scholar working on Signal Processing, Computer Networks and Communications, Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition, having authored 111 papers that have together received 2.7k indexed citations. Recurring topics across this work include Advanced Malware Detection Techniques (63 papers), Network Security and Intrusion Detection (48 papers), Anomaly Detection Techniques and Applications (16 papers), Spam and Phishing Detection (13 papers), Software Testing and Debugging Techniques (10 papers), Digital and Cyber Forensics (10 papers), User Authentication and Security Systems (7 papers) and Internet Traffic Analysis and Secure E-voting (6 papers). The work is most often cited by research in Signal Processing (2.0k citations), Software (349 citations), Computer Networks and Communications (1.8k citations), Information Systems (1.2k citations) and Artificial Intelligence (1.1k citations). Mark Stamp has collaborated with scholars based in United States, Italy and Czechia. Frequent co-authors include Fabio Di Troia, Thomas H. Austin, Richard M. Low, Wing Hung Wong, Corrado Aaron Visaggio, Clyde F. Martin, Douglas S. Reeves, Younghee Park, Katerina Potika and Éric Filiol. Their work appears in journals such as Journal of Computer Virology and Hacking Techniques, International Journal of Security and Networks, Computers & Security, Applied Mathematics and Computation and Expert Systems with Applications.

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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