30 citations · 45 across the 10 of their papers we have counts for
10 papers
Image-Based Techniques and Ensemble Soft Voting for Malware Classification
Sushant Rakesh Lokhande, Fabio Di Troia, Martin Jurecek +1
In this chapter, we investigate image-based malware family classification using an ensemble learning framework and a soft voting strategy. We consider malware binaries that have be…
Feature Analysis of Encrypted Malicious Traffic
Anish Singh Shekhawat, Fabio Di Troia, Mark Stamp
In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication. Antivirus software and fi…
Advancement on Security Applications of Private Intersection Sum Protocol
Yuvaraj Athur Raghuvir, Senthil Govindarajan, Sanjeevi Vijayakumar +2
Secure computation protocols combine inputs from involved parties to generate an output while keeping their inputs private. Private Set Intersection (PSI) is a secure computation p…
A Comparison of Adversarial Learning Techniques for Malware Detection
Pavla Louthánová, Matouš Kozák, Martin Jureček +1
Machine learning has proven to be a useful tool for automated malware detection, but machine learning models have also been shown to be vulnerable to adversarial attacks. This arti…
Hidden Markov Models with Random Restarts vs Boosting for Malware Detection
Aditya Raghavan, Fabio Di Troia, Mark Stamp
Effective and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dra…
Creating Valid Adversarial Examples of Malware
Matouš Kozák, Martin Jureček, Mark Stamp +1
Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results. As a result, antivirus developers are incorporating machine lea…
Classification and Online Clustering of Zero-Day Malware
Olha Jurečková, Martin Jureček, Mark Stamp +2
A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose,…
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…
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…