Mark Stamp

5.3k total citations · 1 hit paper
111 papers, 2.9k citations indexed

About

Mark Stamp is a scholar working on Signal Processing, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Mark Stamp has authored 111 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Signal Processing, 50 papers in Computer Networks and Communications and 46 papers in Artificial Intelligence. Recurrent topics in Mark Stamp's work include Advanced Malware Detection Techniques (62 papers), Network Security and Intrusion Detection (48 papers) and Anomaly Detection Techniques and Applications (16 papers). Mark Stamp is often cited by papers focused on Advanced Malware Detection Techniques (62 papers), Network Security and Intrusion Detection (48 papers) and Anomaly Detection Techniques and Applications (16 papers). Mark Stamp collaborates with scholars based in United States, Italy and Czechia. Mark Stamp's co-authors include Fabio Di Troia, Thomas H. Austin, Richard M. Low, Wing Hung Wong, Peter Stavroulakis, Corrado Aaron Visaggio, Clyde F. Martin, Younghee Park, Douglas S. Reeves and Éric Filiol and has published in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Information Theory and Communications of the ACM.

In The Last Decade

Mark Stamp

106 papers receiving 2.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A comparison of static, dynamic, and hybrid analysis for malware detection

2015 295 citations Fabio Di Troia, Corrado Aaron Visaggio et al. Journal of Computer Virology and Hacking Techniques profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mark Stamp United States	29	2.0k	1.8k	1.2k	1.1k	347	111	2.9k
Felix Freiling Germany	27	2.1k 1.0×	1.9k 1.0×	1.4k 1.2×	1.5k 1.3×	271 0.8×	156	3.1k
Patrick Traynor United States	29	1.4k 0.7×	1.5k 0.8×	1.4k 1.1×	1.3k 1.1×	171 0.5×	109	2.9k
Guoai Xu China	25	890 0.4×	1.2k 0.6×	1.4k 1.1×	725 0.6×	317 0.9×	119	2.4k
Binyu Zang China	30	928 0.5×	1.8k 1.0×	1.7k 1.4×	1.5k 1.3×	178 0.5×	131	3.1k
Kevin Butler United States	25	1.0k 0.5×	1.2k 0.7×	1.1k 0.8×	1.1k 1.0×	109 0.3×	129	2.4k
A. Nur Zincir‐Heywood Canada	28	1.1k 0.6×	2.7k 1.5×	833 0.7×	2.4k 2.1×	160 0.5×	207	3.3k
Zhiqiang Lin United States	31	2.1k 1.0×	1.1k 0.6×	1.5k 1.2×	2.0k 1.7×	544 1.6×	153	3.2k
Helen J. Wang United States	31	2.2k 1.1×	3.1k 1.7×	2.2k 1.8×	2.0k 1.8×	473 1.4×	58	4.9k
Davide Balzarotti France	37	2.8k 1.4×	2.2k 1.2×	2.4k 1.9×	2.3k 2.0×	643 1.9×	113	4.5k

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-authorship network of co-authors of Mark Stamp

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Stamp. A scholar is included among the top collaborators of Mark Stamp 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 Mark Stamp. Mark Stamp is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Stamp, Mark, et al.. (2024). Creating valid adversarial examples of malware. Journal of Computer Virology and Hacking Techniques. 20(4). 607–621. 7 indexed citations

2.

Stamp, Mark, et al.. (2024). Classification and online clustering of zero-day malware. Journal of Computer Virology and Hacking Techniques. 20(4). 579–592. 4 indexed citations

3.

Stamp, Mark, et al.. (2024). Social media bot detection using Dropout-GAN. Journal of Computer Virology and Hacking Techniques. 20(4). 669–680. 2 indexed citations

4.

Stamp, Mark, et al.. (2023). A natural language processing approach to Malware classification. Journal of Computer Virology and Hacking Techniques. 20(1). 173–184. 6 indexed citations

5.

Potika, Katerina, et al.. (2023). A comparison of graph neural networks for malware classification. Journal of Computer Virology and Hacking Techniques. 20(1). 53–69. 4 indexed citations

6.

Troia, Fabio Di, et al.. (2023). Generative adversarial networks and image-based malware classification. Journal of Computer Virology and Hacking Techniques. 19(4). 579–595. 17 indexed citations

7.

Yang, Xinxin & Mark Stamp. (2021). Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS). Computers in Biology and Medicine. 138. 104874–104874. 10 indexed citations

8.

Troia, Fabio Di, et al.. (2018). Deep Learning versus Gist Descriptors for Image-based Malware Classification. 553–561. 57 indexed citations

9.

Troia, Fabio Di, et al.. (2018). Autocorrelation Analysis of Financial Botnet Traffic. 599–606.

10.

Troia, Fabio Di, et al.. (2018). Hidden Markov models with random restarts versus boosting for malware detection. Journal of Computer Virology and Hacking Techniques. 15(2). 97–107. 12 indexed citations

11.

Troia, Fabio Di, Corrado Aaron Visaggio, Thomas H. Austin, & Mark Stamp. (2016). Advanced transcriptase for JavaScript malware. 1–8. 2 indexed citations

12.

Austin, Thomas H., et al.. (2014). Hunting for metamorphic JavaScript malware. Journal of Computer Virology and Hacking Techniques. 11(2). 89–102. 17 indexed citations

13.

Hewitt, D., Mark Stamp, Thomas Barker, & R.H. Marrs. (2011). The potential use of biochar for ameliorating soil fertility in ecological restoration.. Aspects of applied biology. 29–36. 1 indexed citations

14.

Stavroulakis, Peter & Mark Stamp. (2010). Handbook of Information and Communication Security. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 162 indexed citations

15.

Stamp, Mark, et al.. (2008). QuickPay Online Payment Protocol.. Software Engineering and Knowledge Engineering. 223–226. 1 indexed citations

16.

Stamp, Mark, et al.. (2007). SIGABA: Cryptanalysis of the Full Keyspace. Cryptologia. 31(3). 201–222. 3 indexed citations

17.

Stamp, Mark, et al.. (2006). Role Based Access Control and the JXTA Peer-to-Peer Framework.. Security and Management. 390–398. 2 indexed citations

18.

Stamp, Mark. (2003). Digital Rights Management: The Technology Behidn The Hype. Journal of electronic commerce research. 4(3). 102–112. 18 indexed citations

19.

Stamp, Mark & Clyde F. Martin. (1993). An algorithm for the k-error linear complexity of binary sequences with period 2/sup n/. IEEE Transactions on Information Theory. 39(4). 1398–1401. 78 indexed citations

20.

Lewis, T., et al.. (1993). Analysis of a measles epidemic. Statistics in Medicine. 12(3-4). 229–239. 7 indexed citations

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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