475 citations · 555 across the 41 of their papers we have counts for
19 papers · 1 filter
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication
Elliu Huang, Fabio Di Troia, Mark Stamp
Gesture-based authentication has emerged as a non-intrusive, effective means of authenticating users on mobile devices. Typically, such authentication techniques have relied on cla…
Clickbait Detection in YouTube Videos
Ruchira Gothankar, Fabio Di Troia, Mark Stamp
YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted…
Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS)
Xinxin Yang, Mark Stamp
Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, accounting for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdi…
Machine Learning for Malware Evolution Detection
Lolitha Sresta Tupadha, Mark Stamp
Malware evolves over time and antivirus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appropriate countermeas…
Auxiliary-Classifier GAN for Malware Analysis
Rakesh Nagaraju, Mark Stamp
Generative adversarial networks (GAN) are a class of powerful machine learning techniques, where both a generative and discriminative model are trained simultaneously. GANs have be…
Machine Learning-Based Analysis of Free-Text Keystroke Dynamics
Han-Chih Chang, Jianwei Li, Mark Stamp
The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Keystroke dynamics can be used t…
Free-Text Keystroke Dynamics for User Authentication
Jianwei Li, Han-Chih Chang, Mark Stamp
In this research, we consider the problem of verifying user identity based on keystroke dynamics obtained from free-text. We employ a novel feature engineering method that generate…
Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics
Han-Chih Chang, Jianwei Li, Ching-Seh Wu +1
Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. Previous work has demonstrated the feasibility of user authenticat…
An Empirical Analysis of Image-Based Learning Techniques for Malware Classification
Pratikkumar Prajapati, Mark Stamp
In this paper, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilaye…
CNN vs ELM for Image-Based Malware Classification
Mugdha Jain, William Andreopoulos, Mark Stamp
Research in the field of malware classification often relies on machine learning models that are trained on high-level features, such as opcodes, function calls, and control flow g…
On Ensemble Learning
Mark Stamp, Aniket Chandak, Gavin Wong +1
In this paper, we consider ensemble classifiers, that is, machine learning based classifiers that utilize a combination of scoring functions. We provide a framework for categorizin…
Sentiment Analysis for Troll Detection on Weibo
Zidong Jiang, Fabio Di Troia, Mark Stamp
The impact of social media on the modern world is difficult to overstate. Virtually all companies and public figures have social media accounts on popular platforms such as Twitter…
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification
Aniket Chandak, Wendy Lee, Mark Stamp
Word embeddings are often used in natural language processing as a means to quantify relationships between words. More generally, these same word embedding techniques can be used t…
Cluster Analysis of Malware Family Relationships
Samanvitha Basole, Mark Stamp
In this paper, we use -means clustering to analyze various relationships between malware samples. We consider a dataset comprising~20 malware families with~1000 samples per fami…
Word Embedding Techniques for Malware Evolution Detection
Sunhera Paul, Mark Stamp
Malware detection is a critical aspect of information security. One difficulty that arises is that malware often evolves over time. To maintain effective malware detection, it is n…
Universal Adversarial Perturbations and Image Spam Classifiers
Andy Phung, Mark Stamp
As the name suggests, image spam is spam email that has been embedded in an image. Image spam was developed in an effort to evade text-based filters. Modern deep learning-based cla…
Malware Classification with GMM-HMM Models
Jing Zhao, Samanvitha Basole, Mark Stamp
Discrete hidden Markov models (HMM) are often applied to malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture m…
Malware Classification Using Long Short-Term Memory Models
Dennis Dang, Fabio Di Troia, Mark Stamp
Signature and anomaly based techniques are the quintessential approaches to malware detection. However, these techniques have become increasingly ineffective as malware has become…
Malware Classification with Word Embedding Features
Aparna Sunil Kale, Fabio Di Troia, Mark Stamp
Malware classification is an important and challenging problem in information security. Modern malware classification techniques rely on machine learning models that can be trained…