475 citations · 555 across the 41 of their papers we have counts for
13 papers · 2 filters
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…
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…
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…
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…