most citedLearning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning

182 citations · 206 across the 2 of their papers we have counts for

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cs.CR2025★ 24 cited

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers

Robert J. Joyce, Gideon Miller, Phil Roth +5

A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing…

cs.CR2018

EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

Hyrum S. Anderson, Phil Roth

This paper describes EMBER: a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes…

cs.CR2018★ 182 cited

Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning

Hyrum S. Anderson, Anant Kharkar, Bobby Filar +2

Machine learning is a popular approach to signatureless malware detection because it can generalize to never-before-seen malware families and polymorphic strains. This has resulted…