2026 Mark Stamp: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	34	12057	11708	4913	4694	215	5959

Mark Stamp publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Mark Stamp sits on this spectrum.

32 publications 991+

This scientist: 215 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Mark Stamp D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Mark Stamp sits on this spectrum.

30 D-Index 131+

This scientist: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Mark Stamp is affiliated with San Jose State University in the United States and operates primarily within the field of Computer Science, with a significant focus on subfields such as Artificial Intelligence, Signal Processing, Computer Networks and Communications, Information Systems, and Computer Vision and Pattern Recognition.

Their research covers several specialized topics, including:

Advanced Malware Detection Techniques
Network Security and Intrusion Detection
Anomaly Detection Techniques and Applications
Adversarial Robustness in Machine Learning
Spam and Phishing Detection
User Authentication and Security Systems
Digital Media Forensic Detection

Mark Stamp has published both research papers and books, contributing to journals and scientific literature with a focus on malware classification, network security, and machine learning applications in cybersecurity.

Notable recent papers include:

"Convolutional neural networks and extreme learning machines for malware classification," 2020, Journal of Computer Virology and Hacking Techniques
"Darknet traffic classification and adversarial attacks using machine learning," 2023, Computers & Security
"Malware classification with Word2Vec, HMM2Vec, BERT, and ELMo," 2022, Journal of Computer Virology and Hacking Techniques
"Convolutional neural networks for image spam detection," 2020, Information Security Journal A Global Perspective
"Generative adversarial networks and image-based malware classification," 2023, Journal of Computer Virology and Hacking Techniques

Their frequent coauthors include Fabio Di Troia, Martin Jureček, Han-Chih Chang, Olha Jurečková, and Katerina Potika, with collaboration counts ranging from 4 to 28 joint publications.

Mark Stamp has contributed extensively to several publication venues, particularly arXiv (Cornell University) with 52 publications and the Journal of Computer Virology and Hacking Techniques with 11 publications, among other venues such as Computers & Security, Information Security Journal A Global Perspective, and Computers in Biology and Medicine.

In addition to journal articles, Mark Stamp has authored books, including "Artificial Intelligence for Cybersecurity," published by Springer Nature in 2022.

Best Publications

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

Anusha Damodaran;Fabio Di Troia;Corrado Aaron Visaggio;Thomas H. Austin

488
Citations
Hunting for Metamorphic Engines

Wing Wong;Mark Stamp

366
Citations
An algorithm for the k-error linear complexity of binary sequences with period 2/sup n/

M. Stamp;C.F. Martin

313
Citations
A Revealing Introduction to Hidden Markov Models

Mark Stamp

308
Citations
Handbook of Information and Communication Security

Peter Stavroulakis;Mark Stamp

256
Citations
Opcode graph similarity and metamorphic detection

Neha Runwal;Richard M. Low;Mark Stamp

203
Citations
Structural entropy and metamorphic malware

Donabelle Baysa;Richard M. Low;Mark Stamp

159
Citations
Hidden Markov models for malware classification

Chinmayee Annachhatre;Thomas H. Austin;Mark Stamp

158
Citations
Profile hidden Markov models and metamorphic virus detection

Srilatha Attaluri;Scott McGhee;Mark Stamp

153
Citations
Hunting for undetectable metamorphic viruses

Da Lin;Mark Stamp

152
Citations
Applied Cryptanalysis: Breaking Ciphers in the Real World

Mark Stamp;Richard M. Low

149
Citations
Introduction to Machine Learning with Applications in Information Security

Mark Stamp

138
Citations
Deriving common malware behavior through graph clustering

Younghee Park;Douglas S. Reeves;Mark Stamp

137
Citations
Chi-squared distance and metamorphic virus detection

Annie H. Toderici;Mark Stamp

123
Citations
Exploring Hidden Markov Models for Virus Analysis: A Semantic Approach

T. H. Austin;E. Filiol;S. Josse;M. Stamp

107
Citations
Risks of monoculture

Mark Stamp

96
Citations
Metamorphic worm that carries its own morphing engine

Sudarshan Madenur Sridhara;Mark Stamp

94
Citations
Simple substitution distance and metamorphic detection

Gayathri Shanmugam;Richard M. Low;Mark Stamp

90
Citations
Feature analysis of encrypted malicious traffic

Anish Singh Shekhawat;Fabio Di Troia;Mark Stamp

88
Citations
Transfer Learning for Image-Based Malware Classification

Niket Bhodia;Pratikkumar Prajapati;Fabio Di Troia;Mark Stamp

86
Citations
Detecting Malware Evolution Using Support Vector Machines

Mayuri Wadkar;Fabio Di Troia;Mark Stamp

84
Citations
Deep learning versus gist descriptors for image-based malware classification

Sravani Yajamanam;Vikash Raja Samuel Selvin;Fabio Di Troia;Mark Stamp

68
Citations

Frequent Co-Authors

Corrado Aaron Visaggio University of Sannio

Mamoun Alazab Charles Darwin University

Cormac Flanagan University of California, Santa Cruz

Douglas S. Reeves North Carolina State University

External Links

Personal website of Mark Stamp

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