Generation & Evaluation of Adversarial Examples for Malware Obfuscation

Abstract

There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to adversarial examples allows malicious actors to evade classifiers. Adversarial examples are usually generated by adding small perturbations to the input that are unrecognizable to humans, but the same approach is not effective with malware. In general, these perturbations cause changes in the byte sequences that change the initial functionality or result in un-executable binaries. We present a generative model for executable adversarial malware examples using obfuscation that achieves a high misclassification rate, up to 100% and 98% in white-box and black-box settings respectively, and demonstrates transferability. We further evaluate the effectiveness of the proposed method by reporting insignificant change in the evasion rate of our adversarial examples against popular defense strategies.

Preprint

References in corpus (10)

• XGBoost: A Scalable Tree Boosting System
• Distilling the Knowledge in a Neural Network
• Explaining and Harnessing Adversarial Examples
• Intriguing properties of neural networks
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
• Adversarial Perturbations Against Deep Neural Networks for Malware Classification
• Microsoft Malware Classification Challenge
• Imbalanced Malware Images Classification: a CNN based Approach
• Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
• Black-Box Attacks against RNN based Malware Detection Algorithms

Cited by in corpus (5)

• Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
• MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers
• Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model
• Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection
• Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security