World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
5959
World Ranking
12059
National Ranking
4913

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

  • Hunting for Metamorphic Engines

    Wing Wong;Mark Stamp

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

    M. Stamp;C.F. Martin

  • A Revealing Introduction to Hidden Markov Models

    Mark Stamp

  • Handbook of Information and Communication Security

    Peter Stavroulakis;Mark Stamp

  • Opcode graph similarity and metamorphic detection

    Neha Runwal;Richard M. Low;Mark Stamp

  • Structural entropy and metamorphic malware

    Donabelle Baysa;Richard M. Low;Mark Stamp

  • Hidden Markov models for malware classification

    Chinmayee Annachhatre;Thomas H. Austin;Mark Stamp

  • Profile hidden Markov models and metamorphic virus detection

    Srilatha Attaluri;Scott McGhee;Mark Stamp

  • Hunting for undetectable metamorphic viruses

    Da Lin;Mark Stamp

  • Applied Cryptanalysis: Breaking Ciphers in the Real World

    Mark Stamp;Richard M. Low

  • Introduction to Machine Learning with Applications in Information Security

    Mark Stamp

  • Deriving common malware behavior through graph clustering

    Younghee Park;Douglas S. Reeves;Mark Stamp

  • Chi-squared distance and metamorphic virus detection

    Annie H. Toderici;Mark Stamp

  • Exploring Hidden Markov Models for Virus Analysis: A Semantic Approach

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

  • Risks of monoculture

    Mark Stamp

  • Metamorphic worm that carries its own morphing engine

    Sudarshan Madenur Sridhara;Mark Stamp

  • Simple substitution distance and metamorphic detection

    Gayathri Shanmugam;Richard M. Low;Mark Stamp

  • Feature analysis of encrypted malicious traffic

    Anish Singh Shekhawat;Fabio Di Troia;Mark Stamp

  • Transfer Learning for Image-Based Malware Classification

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

  • Detecting Malware Evolution Using Support Vector Machines

    Mayuri Wadkar;Fabio Di Troia;Mark Stamp

  • Deep learning versus gist descriptors for image-based malware classification

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

Frequent Co-Authors

Corrado Aaron Visaggio
Corrado Aaron Visaggio University of Sannio
Mamoun Alazab
Mamoun Alazab Charles Darwin University
Cormac Flanagan
Cormac Flanagan University of California, Santa Cruz
Douglas S. Reeves
Douglas S. Reeves North Carolina State University

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