MDEA: Malware Detection with Evolutionary Adversarial Learning

Abstract

Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify as benign or malicious files. Even though this approach has proven effective against dynamic changes, such as encrypting, obfuscating and packing techniques, it is vulnerable to specific evasion attacks where that small changes in the input data cause misclassification at test time. This paper proposes a new approach: MDEA, an Adversarial Malware Detection model uses evolutionary optimization to create attack samples to make the network robust against evasion attacks. By retraining the model with the evolved malware samples, its performance improves a significant margin.

arXiv comment8 pages, 6 figures

References in corpus (6)

• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
• Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
• Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning
• Evolutionary Generative Adversarial Networks
• The advantages of multiple classes for reducing overfitting from test set reuse