M. Stamp

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Profile hidden Markov models and metamorphic virus detection

Mark Stamp, Scott McGhee, Srilatha Attaluri, 2009, Journal in Computer Virology.

Digital Rights Management: The Technology Behidn The Hype

Mark Stamp, M. Stamp, 2003 .

Solvable problems in enterprise digital rights management

Mark Stamp, E. John Sebes, M. Stamp, 2007, Inf. Manag. Comput. Secur..

Digital Rights Management: The Technology Behind the Hype

Mark Stamp, M. Stamp, 2003, J. Electron. Commer. Res..

Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS)

Mark Stamp, Xinxin Yang, M. Stamp, 2021, Comput. Biol. Medicine.

Cryptanalysis of Typex

Mark Stamp, Richard M. Low, Kelly Chang, 2014, Cryptologia.

Clickbait Detection in YouTube Videos pdf

Mark Stamp, Fabio Di Troia, Ruchira Gothankar, 2021, ArXiv.

Pokemon Cards and the Shortest Common Superstring

Mark Stamp, Austin E Stamp, M. Stamp, 2003 .

Pokemon Trading Card Sequences

M. Stamp, A. E. Stamp, 2003 .

A New Dataset for Smartphone Gesture-based Authentication

Mark Stamp, Fabio Di Troia, Elliu Huang, 2021, ICISSP.

Applied Cryptanalysis: Breaking Ciphers in the Real World

Mark Stamp, Richard M. Low, M. Stamp, 2007 .

Machine Learning for Malware Evolution Detection pdf

Mark Stamp, Lolitha Sresta Tupadha, M. Stamp, 2021, ArXiv.

Word Embedding Techniques for Malware Evolution Detection

M. Stamp, Sunhera Paul, 2020, Malware Analysis Using Artificial Intelligence and Deep Learning.

Information Security Textbook

Mark Stamp, M. Stamp, 2005 .

Machine Learning-Based Analysis of Free-Text Keystroke Dynamics pdf

Mark Stamp, Han-Chih Chang, Jianwei Li, 2021, ArXiv.

Hunting for undetectable metamorphic viruses

Mark Stamp, Da Lin, M. Stamp, 2011, Journal in Computer Virology.

Metamorphic Code from LLVM IR Bytecode

M. Stamp, Teja Tamboli, 2013 .

Digital Rights Management : The Technology Behind the Hype Mark Stamp 20080

M. Stamp, 2002 .

A Model for the Optimal Control of a Measles Epidemic

L. Allen, Clyde F. Martin, M. Stamp, 1993 .

Once Upon a Time-Memory Tradeoff

M. Stamp, 2003 .

Hidden Markov Models with Momentum

Fabio Di Troia, M. Stamp, Andrew Miller, 2022, ArXiv.

Generative adversarial networks and image-based malware classification

Fabio Di Troia, M. Stamp, H. Nguyen, 2022, Journal of Computer Virology and Hacking Techniques.

Darknet Traffic Classification and Adversarial Attacks

M. Stamp, Nhien Rust-Nguyen, 2022, 2206.06371.

Multifamily malware models

Fabio Di Troia, M. Stamp, Samanvitha Basole, 2020, Journal of Computer Virology and Hacking Techniques.

Alphabet Soup of Deep Learning Topics

M. Stamp, 2019 .

A generalized linear complexity

M. Stamp, 1992 .

Simple Authentication Protocols

Mark Stamp, M. Stamp, 2005 .

A Reassuring Introduction to Support Vector Machines

Mark Stamp, M. Stamp, 2017 .

Software Flaws and Malware

Mark Stamp, M. Stamp, 2005, Information Security.

Robust Watermarking using Hidden Markov Models

Mark Stamp, Mausami Mungale, M. Stamp, 2011 .

A Full Frontal View of Profile Hidden Markov Models

Mark Stamp, M. Stamp, 2017 .

A Comprehensible Collection of Clustering Concepts

Mark Stamp, M. Stamp, 2017 .

Appendix: A‐1 Network Security Basics

Mark Stamp, M. Stamp, 2011 .

BootBandit: A macOS bootloader attack

Mark Stamp, Armen Boursalian, M. Stamp, 2019, Engineering Reports.

Automatic NFC message sending good and evil

Mark Stamp, Nikki Benecke Brandt, M. Stamp, 2014 .

QuickPay Online Payment Protocol

Mark Stamp, Jian Dai, M. Stamp, 2008, SEKE.

Insecurity in Software

Mark Stamp, M. Stamp, 2005, Information Security.

Operating Systems and Security

Mark Stamp, M. Stamp, 2005 .

Real‐World Security Protocols

Mark Stamp, M. Stamp, 2005 .

Mark Stamp, Mausami Mungale, M. Stamp, 2012 .

Evaluations and Enhancements in 802.11n WLANs â•fi Error-Sensitive Adaptive Frame Aggregation

Mark Stamp, Sjsu ScholarWorks, Melody Moh, 2014 .

World War II Ciphers

Mark Stamp, Richard M. Low, M. Stamp, 2007 .

Hash Functions and Other Topics

Mark Stamp, M. Stamp, 2005 .

Circular Binary Sequences

Mark Stamp, M. Stamp, 1992, SIAM Rev..

Public Key Crypto

Mark Stamp, M. Stamp, 2005 .

Symmetric Key Crypto

Mark Stamp, M. Stamp, 2005 .

Improved image spam detection

Mark Stamp, A. Chavda, M. Stamp, 2017 .

Introduction to Machine Learning with Applications in Information Security

Mark Stamp, M. Stamp, 2017 .

Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks

M. Stamp, Eric Liang, 2022, ArXiv.

Darknet Traffic Classification and Adversarial Attacks

M. Stamp, Nhien Rust-Nguyen, 2022, ArXiv.

Malware classification with Word2Vec, HMM2Vec, BERT, and ELMo

Aparna Sunil Kale, M. Stamp, Vinay Pandya, 2022, Journal of Computer Virology and Hacking Techniques.

Ensemble Learning

R. Buyya, M. Stamp, Jitendra Kumar, 2019, Encyclopedia of Biometrics.

Boosting Your Knowledge of Boosting

M. Stamp, 2018 .

Evaluations and Enhancements in 802.11n WLANs â•fi Error-Sensitive Adaptive Frame Aggregation

M. Stamp, S. Khuri, Sjsu ScholarWorks, 2014 .

Metamorphic code generation from LLVM bytecode

Thomas H. Austin, M. Stamp, Teja Tamboli, 2014, Journal of Computer Virology and Hacking Techniques.

Classification and generalization of pseudo-random number generators

Clyde F. Martin, M. Stamp, 1990 .

Constructing Polynomials over Finite Fields

M. Stamp, 1989 .

Social Networking for Botnet Command and Control

Mark Stamp, Kevin Ross, Annie H. Toderici, 2013 .

Analysis of Infinite Dimensional Dynamic Systems with Nonlinear Observation over a Finite Field

M. Stamp, C. Martin, 1991 .

Universal Adversarial Perturbations and Image Spam Classifiers

M. Stamp, Andy Phung, 2020, Malware Analysis Using Artificial Intelligence and Deep Learning.

Discrete observability and numerical quadrature

Mark Stamp, Clyde F. Martin, M. Stamp, 1991 .

Convolutional neural networks for image spam detection

Mark Stamp, Fabio Di Troia, Katerina Potika, 2020, Inf. Secur. J. A Glob. Perspect..

Image spam analysis and detection

Mark Stamp, Annapurna Annadatha, M. Stamp, 2018, Journal of Computer Virology and Hacking Techniques.

A Comparison of Graph Neural Networks for Malware Classification

M. Stamp, Vrinda Malhotra, Katerina Potika, 2023, Journal of Computer Virology and Hacking Techniques.

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

Mark Stamp, Fabio Di Troia, Corrado Aaron Visaggio, 2015, Journal of Computer Virology and Hacking Techniques.

Gaussian Quadrature and Linear Systems

M. Stamp, C. Martin, 1991 .

Handbook of Information and Communication Security

Mark Stamp, Peter Stavroulakis, M. Stamp, 2010, Handbook of Information and Communication Security.

Convolutional neural networks and extreme learning machines for malware classification

Mark Stamp, Mugdha Jain, William Andreopoulos, 2020, Journal of Computer Virology and Hacking Techniques.

Transfer Learning for Image-Based Malware Classification

Mark Stamp, Fabio Di Troia, Niket Bhodia, 2019, ICISSP.

Hidden Markov models for malware classification

Mark Stamp, Thomas H. Austin, Chinmayee Annachhatre, 2015, Journal of Computer Virology and Hacking Techniques.

Hunting for metamorphic JavaScript malware

Mark Stamp, Thomas H. Austin, Mangesh Musale, 2015, Journal of Computer Virology and Hacking Techniques.

Hunting for metamorphic engines

Mark Stamp, Wing Wong, M. Stamp, 2006, Journal in Computer Virology.

Support vector machines and malware detection

Mark Stamp, Fabio Di Troia, Corrado Aaron Visaggio, 2016, Journal of Computer Virology and Hacking Techniques.

Acoustic Gait Analysis using Support Vector Machines

Mark Stamp, Fabio Di Troia, Jasper Huang, 2018, ICISSP.

Support Vector Machines for Image Spam Analysis

Mark Stamp, Fabio Di Troia, Katerina Potika, 2018, ICETE.

Deep Learning versus Gist Descriptors for Image-based Malware Classification

Mark Stamp, Fabio Di Troia, Sravani Yajamanam, 2018, ICISSP.

Structural entropy and metamorphic malware

Mark Stamp, Richard M. Low, Donabelle Baysa, 2013, Journal of Computer Virology and Hacking Techniques.

A comparison of machine learning classifiers for acoustic gait analysis

Mark Stamp, Fabio Di Troia, Jasper Huang, 2018 .

Risks of monoculture

Mark Stamp, M. Stamp, 2004, CACM.

Carrying a rucksack on either shoulder or the back, does it matter? Load induced functional scoliosis in "normal" young subjects.

J Bettany-Saltikov, M. Stamp, J. Bettany-Saltikov, 2008, Studies in health technology and informatics.

Singular value decomposition and metamorphic detection

Mark Stamp, Thomas H. Austin, Ranjith Kumar Jidigam, 2015, Journal of Computer Virology and Hacking Techniques.

Compression-based analysis of metamorphic malware

Mark Stamp, Thomas H. Austin, Jared Lee, 2015, Int. J. Secur. Networks.

Dueling hidden Markov models for virus analysis

Eric Filiol, Mark Stamp, Thomas H. Austin, 2015, Journal of Computer Virology and Hacking Techniques.

Eigenvalue analysis for metamorphic detection

Mark Stamp, Younghee Park, Sayali Deshpande, 2014, Journal of Computer Virology and Hacking Techniques.

Metamorphic worm that carries its own morphing engine

Mark Stamp, Sudarshan Madenur Sridhara, M. Stamp, 2013, Journal of Computer Virology and Hacking Techniques.

Chi-squared distance and metamorphic virus detection

Mark Stamp, Annie H. Toderici, M. Stamp, 2013, Journal of Computer Virology and Hacking Techniques.

Function Call Graphs Versus Machine Learning for Malware Detection

Mark Stamp, Fabio Di Troia, Thomas H. Austin, 2018, Guide to Vulnerability Analysis for Computer Networks and Systems.

Practical Detection of Metamorphic Computer Viruses

Mark Stamp, Sjsu ScholarWorks, Robert Chun, 2008 .

Malware Classification with GMM-HMM Models pdf

Mark Stamp, Jing Zhao, Samanvitha Basole, 2021, ICISSP.

Analysis of a measles epidemic.

L. Allen, M. Stamp, L J Allen, 1993, Statistics in medicine.

Stochastic analysis of vaccination strategies

Mark Stamp, Clyde F. Martin, Linda J. S. Allen, 1992 .

A natural language processing approach to Malware classification

M. Stamp, Olha Jurecková, Ritik Mehta, 2023, J. Comput. Virol. Hacking Tech..

CNN vs ELM for Image-Based Malware Classification pdf

Mark Stamp, Mugdha Jain, William Andreopoulos, 2021, ArXiv.

Malware Classification using Long Short-term Memory Models

Fabio Di Troia, M. Stamp, D. Dang, 2021, ICISSP.

A Revealing Introduction to Hidden Markov Models

Mark Stamp, M. Stamp, 2017 .

An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

M. Stamp, Pratikkumar Prajapati, 2020, Malware Analysis Using Artificial Intelligence and Deep Learning.

Clustering for malware classification

Mark Stamp, Fabio Di Troia, Corrado Aaron Visaggio, 2017, Journal of Computer Virology and Hacking Techniques.

Deriving common malware behavior through graph clustering

Douglas S. Reeves, Younghee Park, D. Reeves, 2011, ASIACCS '11.

Malware Detection Using Dynamic Birthmarks

Mark Stamp, Fabio Di Troia, Corrado Aaron Visaggio, 2016, IWSPA@CODASPY.

Exploring Hidden Markov Models for Virus Analysis: A Semantic Approach

Mark Stamp, Thomas H. Austin, Eric Filiol, 2013, 2013 46th Hawaii International Conference on System Sciences.

Feature analysis of encrypted malicious traffic

Mark Stamp, Fabio Di Troia, Anish Singh Shekhawat, 2019, Expert Syst. Appl..

Auxiliary-Classifier GAN for Malware Analysis pdf

Mark Stamp, Rakesh Nagaraju, M. Stamp, 2021, ArXiv.

Information security - principles and practice

Mark Stamp, M. Stamp, 2005 .

Sentiment Analysis for Troll Detection on Weibo

Fabio Di Troia, M. Stamp, Zidong Jiang, 2020, Malware Analysis Using Artificial Intelligence and Deep Learning.

Classic cryptanalysis using hidden Markov models

Mark Stamp, Fabio Di Troia, Corrado Aaron Visaggio, 2017, Cryptologia.

Detecting Encrypted and Polymorphic Malware Using Hidden Markov Models

Mark Stamp, Fabio Di Troia, Katerina Potika, 2018, Guide to Vulnerability Analysis for Computer Networks and Systems.

Detecting Undetectable Metamorphic Viruses

Mark Stamp, M. Stamp, Sujandharan Venkatachalam, 2011 .

A Revealing Introduction to Hidden Markov Models Mark Stamp

Mark Stamp, M. Stamp, 2004 .

HTTP attack detection using n-gram analysis

Mark Stamp, Kevin Ross, Richard M. Low, 2014, Comput. Secur..

Detecting malware evolution using support vector machines

Mark Stamp, Fabio Di Troia, Mayuri Wadkar, 2020, Expert Syst. Appl..

A Dynamic Heuristic Method for Detecting Packed Malware Using Naive Bayes

Mark Stamp, Ehab M. Alkhateeb, M. Stamp, 2019, 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA).

Urn model simulations of a sexually transmitted disease epidemic

L. Allen, M. Stamp, C. Martin, 1995 .

Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication pdf

Mark Stamp, Fabio Di Troia, Elliu Huang, 2021, ArXiv.

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

Mark Stamp, Clyde F. Martin, M. Stamp, 1993, IEEE Trans. Inf. Theory.

A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification

M. Stamp, Aniket Chandak, Wendy Lee, 2020, Malware Analysis Using Artificial Intelligence and Deep Learning.

Robust Hashing for Image-based Malware Classification

Mark Stamp, Fabio Di Troia, Wei-Chung Huang, 2018, ICETE.

Advanced transcriptase for JavaScript malware

Mark Stamp, Fabio Di Troia, Corrado Aaron Visaggio, 2016, 2016 11th International Conference on Malicious and Unwanted Software (MALWARE).

On the Steganographic Capacity of Selected Learning Models

M. Stamp, Nathan Zhang, Yash Seth, 2023, ArXiv.

A Comparison of Adversarial Learning Techniques for Malware Detection

M. Stamp, M. Jureček, Pavla Louth'anov'a, 2023, ArXiv.

A Survey of Machine Learning Algorithms and Their Application in Information Security

Mark Stamp, M. Stamp, 2018, Guide to Vulnerability Analysis for Computer Networks and Systems.

Malware Classification with Word Embedding Features

Mark Stamp, Fabio Di Troia, Aparna Sunil Kale, 2021, ICISSP.

A Selective Survey of Deep Learning Techniques and Their Application to Malware Analysis

M. Stamp, 2020, Malware Analysis Using Artificial Intelligence and Deep Learning.

Masquerade Detection on Mobile Devices

Mark Stamp, Fabio Di Troia, Swathi Nambiar Kadala Manikoth, 2018, Guide to Vulnerability Analysis for Computer Networks and Systems.

Hunting for metamorphic engines

M. Stamp, Wing Wong, 2006, Journal in Computer Virology.

Static and Dynamic Analysis of Android Malware

Mark Stamp, Fabio Di Troia, Ankita Kapratwar, 2017, ICISSP.

Intrusion Detection and CAN Vehicle Networks

Fabio Di Troia, M. Stamp, Ashraf Saber, 2020, Digital Forensic Investigation of Internet of Things (IoT) Devices.

Hunting for Pirated Software Using Metamorphic Analysis

Mark Stamp, Hardikkumar Rana, M. Stamp, 2014, Inf. Secur. J. A Glob. Perspect..

An analysis of the transmission of chlamydia in a closed population

L. Allen, M. Stamp, C. Martin, 1996 .

An analysis of Android adware

Mark Stamp, Fabio Di Troia, Katerina Potika, 2018, Journal of Computer Virology and Hacking Techniques.

Social Media Bot Detection using Dropout-GAN

M. Stamp, M. Jureček, Anant Shukla, 2023, Journal of Computer Virology and Hacking Techniques.

Steganographic Capacity of Deep Learning Models

M. Stamp, Dong Li, Lei Zhang, 2023, ArXiv.