SelectedWorks - Mark Stamp

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Selected Works of Mark Stamp

Professor, Department of Computer Science

Dr. Stamp is a Professor in the Department of Computer Science at San José State University. Please refer to his current CV.

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About Mark Stamp

Dr. Stamp is a Professor in the Department of Computer Science at San José State University. Please refer to his current CV.

An empirical analysis of the shift and scale parameters in ...

Information Sciences (2023)

Yashna Peerthum and Mark Stamp

Batch Normalization (BatchNorm) is a technique that improves the training of deep neural networks, especially Convolutional Neural Networks (CNN). It ...

Darknet traffic classification and adversarial attacks using machine learning

Computers and Security (2023)

Nhien Rust-Nguyen, Shruti Sharma and Mark Stamp

The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning ...

Generative adversarial networks and image-based malware classification

Journal of Computer Virology and Hacking Techniques (2023)

Huy Nguyen, Fabio Di Troia, Genya Ishigaki and Mark Stamp

For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In ...

Predicting pedestrian crosswalk behavior using Convolutional Neural Networks

Traffic Injury Prevention (2023)

Eric Liang and Mark Stamp

Objective: Pedestrian accidents contribute significantly to the high number of annual traffic casualties. It is therefore crucial for pedestrians to ...

Introduction to Machine Learning with Applications in Information Security: Second ...

Faculty Research, Scholarly, and Creative Activity (2022)

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning ...

Contribution to Book

Auxiliary-Classifier GAN for Malware Analysis

Advances in Information Security (2022)

Rakesh Nagaraju and Mark Stamp

Generative adversarial networks (GAN) are a class of powerful machine learning techniques, where both a generative and discriminative model are ...

Contribution to Book

Clickbait Detection for YouTube Videos

Advances in Information Security (2022)

Ruchira Gothankar, Fabio Di Troia and Mark Stamp

YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the ...

Contribution to Book

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

Advances in Information Security (2022)

Elliu Huang, Fabio Di Troia and Mark Stamp

Gesture-based authentication has emerged as a non-intrusive, effective means of authenticating users on mobile devices. Typically, such authentication techniques have ...

Contribution to Book

Free-Text Keystroke Dynamics for User Authentication

Advances in Information Security (2022)

Jianwei Li, Han Chih Chang and Mark Stamp

In this research, we consider the problem of verifying user identity based on keystroke dynamics obtained from free-text. We employ ...

Contribution to Book

Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

Advances in Information Security (2022)

Han Chih Chang, Jianwei Li, Ching Seh Wu and Mark Stamp

Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. Previous ...

Contribution to Book

Machine Learning for Malware Evolution Detection

Advances in Information Security (2022)

Lolitha Sresta Tupadha and Mark Stamp

Malware evolves over time and antivirus must adapt to such evolution. Hence, it is critical to detect those points in ...

Contribution to Book

Machine Learning-Based Analysis of Free-Text Keystroke Dynamics

Advances in Information Security (2022)

Han Chih Chang, Jianwei Li and Mark Stamp

The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Keystroke dynamics ...

Robustness of Image-Based Malware Analysis

Communications in Computer and Information Science (2022)

Katrina Tran, Fabio Di Troia and Mark Stamp

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising ...

Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS)

Computers in Biology and Medicine (2021)

Xinxin Yang and Mark Stamp

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients ...

Convolutional neural networks and extreme learning machines for malware classification

Journal of Computer Virology and Hacking Techniques (2020)

Mugdha Jain, William Andreopoulos and Mark Stamp

Research in the field of malware classification often relies on machine learning models that are trained on high-level features, such ...

Convolutional neural networks for image spam detection

Information Security Journal: A Global Perspective (2020)

Tazmina Sharmin, Fabio Di Troia, Katerina Potika and Mark Stamp

Spam can be defined as unsolicited bulk e-mail. In an effort to evade text-based filters, spammers sometimes embed spam text ...

Detecting malware evolution using support vector machines

Expert Systems with Applications (2020)

Mayuri Wadkar, Fabio Di Troia and Mark Stamp

Malware families typically evolve over a period of time. Differences between malware samples within a single family can originate from ...

Black box analysis of android malware detectors

Guruswamy Nellaivadivelu, Fabio Di Troia and Mark Stamp

If a malware detector relies heavily on a feature that is obfuscated in a given malware sample, then the detector ...

Multifamily malware models

Journal of Computer Virology and Hacking Techniques (2020)

Samanvitha Basole, Fabio Di Troia and Mark Stamp

When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the ...

BootBandit: A macOS bootloader attack

Engineering Reports (2019)

Armen Boursalian and Mark Stamp

Historically, the boot phase on personal computers left systems in a relatively vulnerable state. Because traditional antivirus software runs within ...

Contribution to Book

Function Call Graphs Versus Machine Learning for Malware Detection

Guide to Vulnerability Analysis for Computer Networks and Systems: An Artificial Intelligence Approach (2018)

Deebiga Rajeswaran, Fabio Di Troia, Thomas H. Austin and Mark Stamp

Recent work has shown that a function call graph technique can perform well on some challenging malware detection problems. In ...

Contribution to Book

Masquerade Detection on Mobile Devices

Guide to Vulnerability Analysis for Computer Networks and Systems: An Artificial Intelligence Approach (2018)

Swathi Nambiar Kadala Manikoth, Fabio Di Troia and Mark Stamp

A masquerade is a type of attack where an intruder attempts to avoid detection by impersonating an authorized user of ...

Image spam analysis and detection

Journal of Computer Virology and Hacking Techniques (2018)

Mark Stamp and Annapurna Annadatha

Image spam is unsolicited bulk email, where the message is embedded in an image. Spammers use such images to evade ...

Contribution to Book

Autocorrelation Analysis of Financial Botnet Traffic

Proceedings of the 4th International Conference on Information Systems Security and Privacy (2018)

Prathiba Nagarajan, Fabio Di Troia, Thomas H. Austin and Mark Stamp

A botnet consists of a network of infected computers that can be controlled remotely via a command and control (C&C) ...

Contribution to Book

Deep learning versus gist descriptors for image-based malware classification

Proceedings of the 4th International Conference on Information Systems Security and Privacy (2018)

Sravani Yajamanam, Vikash Raja Samuel Selvin, Fabio Di Troia and Mark Stamp

Image features known as ``gist descriptors'' have recently been applied to the malware classification problem. In this research, we implement, ...