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20142022
most citedAdversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection

133 citations · 195 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2022★ 1 cited

Efficient Malware Analysis Using Metric Embeddings

Ethan M. Rudd, David Krisiloff, Scott Coull +3

In this paper, we explore the use of metric learning to embed Windows PE files in a low-dimensional vector space for downstream use in a variety of applications, including malware…

cs.CR2022★ 2 cited

On the Limitations of Continual Learning for Malware Classification

Mohammad Saidur Rahman, Scott E. Coull, Matthew Wright

Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evoluti…

cs.CR2020★ 133 cited

Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection

Luca Demetrio, Scott E. Coull, Battista Biggio +3

Recent work has shown that adversarial Windows malware samples - referred to as adversarial EXEmples in this paper - can bypass machine learning-based detection relying on static c…

cs.CR2020★ 56 cited

Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers

Giorgio Severi, Jim Meyer, Scott Coull +1

Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we stud…

cs.LG2019

Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification

Scott E. Coull, Christopher Gardner

Feature engineering is one of the most costly aspects of developing effective machine learning models, and that cost is even greater in specialized problem domains, like malware cl…

cs.LG2018

Exploring Adversarial Examples in Malware Detection

Octavian Suciu, Scott E. Coull, Jeffrey Johns

The convolutional neural network (CNN) architecture is increasingly being applied to new domains, such as malware detection, where it is able to learn malicious behavior from raw b…

cs.CR2014★ 3 cited

Privacy Failures in Encrypted Messaging Services: Apple iMessage and Beyond

Scott Coull, Kevin Dyer

Instant messaging services are quickly becoming the most dominant form of communication among consumers around the world. Apple iMessage, for example, handles over 2 billion messag…