Adversarial Detection of Flash Malware: Limitations and Open Issues

Abstract

During the past four years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash disclosed in the wild. Research has shown that machine learning can be successfully used to detect Flash malware by leveraging static analysis to extract information from the structure of the file or its bytecode. However, the robustness of Flash malware detectors against well-crafted evasion attempts - also known as adversarial examples - has never been investigated. In this paper, we propose a security evaluation of a novel, representative Flash detector that embeds a combination of the prominent, static features employed by state-of-the-art tools. In particular, we discuss how to craft adversarial Flash malware examples, showing that it suffices to manipulate the corresponding source malware samples slightly to evade detection. We then empirically demonstrate that popular defense techniques proposed to mitigate evasion attempts, including re-training on adversarial examples, may not always be sufficient to ensure robustness. We argue that this occurs when the feature vectors extracted from adversarial examples become indistinguishable from those of benign data, meaning that the given feature representation is intrinsically vulnerable. In this respect, we are the first to formally define and quantitatively characterize this vulnerability, highlighting when an attack can be countered by solely improving the security of the learning algorithm, or when it requires also considering additional features. We conclude the paper by suggesting alternative research directions to improve the security of learning-based Flash malware detectors.

References in corpus (8)

• ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
• Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
• Poisoning Attacks against Support Vector Machines
• Stealing Machine Learning Models via Prediction APIs
• Security Evaluation of Pattern Classifiers under Attack
• Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
• On the Effectiveness of System API-Related Information for Android Ransomware Detection
• Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

Cited by in corpus (3)

• EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
• Arms Race in Adversarial Malware Detection: A Survey
• MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers