475 citations · 555 across the 41 of their papers we have counts for
7 papers · 1 filter
Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks
Eric Liang, Mark Stamp
A common yet potentially dangerous task is the act of crossing the street. Pedestrian accidents contribute a significant amount to the high number of annual traffic casualties, whi…
Multifamily Malware Models
Samanvitha Basole, Fabio Di Troia, Mark Stamp
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to…
Darknet Traffic Classification and Adversarial Attacks
Nhien Rust-Nguyen, Mark Stamp
The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detectio…
Generative Adversarial Networks and Image-Based Malware Classification
Huy Nguyen, Fabio Di Troia, Genya Ishigaki +1
For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract feature…
Hidden Markov Models with Momentum
Andrew Miller, Fabio Di Troia, Mark Stamp
Momentum is a popular technique for improving convergence rates during gradient descent. In this research, we experiment with adding momentum to the Baum-Welch expectation-maximiza…
Convolutional Neural Networks for Image Spam Detection
Tazmina Sharmin, Fabio Di Troia, Katerina Potika +1
Spam can be defined as unsolicited bulk email. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this…
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection
Anusha Damodaran, Fabio Di Troia, Visaggio Aaron Corrado +2
In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dyna…