Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables (1803.04173v1)

Abstract: Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn models that discriminate between benign and malicious software. However, it has also been shown that machine learning and deep neural networks can be fooled by evasion attacks (also referred to as adversarial examples), i.e., small changes to the input data that cause misclassification at test time. In this work, we investigate the vulnerability of malware detection methods that use deep networks to learn from raw bytes. We propose a gradient-based attack that is capable of evading a recently-proposed deep network suited to this purpose by only changing few specific bytes at the end of each malware sample, while preserving its intrusive functionality. Promising results show that our adversarial malware binaries evade the targeted network with high probability, even though less than 1% of their bytes are modified.

• The paper introduces a gradient-based adversarial attack that modifies less than 1% of executable bytes to evade the MalConv model.
• Experiments on 13,195 Windows executables reveal an evasion rate up to 60%, significantly reducing detection accuracy.
• The study emphasizes the need for more robust, semantically-aware approaches to counter adversarial malware manipulation.

Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables

This paper investigates a critical vulnerability in machine learning-based malware detection strategies, particularly those leveraging deep neural networks and operating directly on raw bytes of executable files. The paper focuses on evasion attacks, i.e., small targeted alterations to binary files designed to mislead malware detectors into classifying malicious files as benign.

Key Contributions

The authors introduce a gradient-based attack method specifically targeting the MalConv architecture, a deep neural network trained on raw bytes for malware detection, first presented by Raff et al. The proposed methodology allows adversarial modifications with high evasion success while preserving the malicious functionality of the binaries. This is achieved by modifying less than 1% of the bytes—specifically, by appending certain bytes at the end of the file that are carefully selected based on the gradient of the network. Remarkably, these modifications decrease the detection accuracy of MalConv by over 50%.

Experimental Evaluation

The experimental setup involves crafting adversarial binaries from a dataset comprising 13,195 Windows Portable Executable samples. The paper demonstrates that the MalConv's accuracy drops significantly—up to an evasion rate of 60% for the crafted adversarial samples—when 10,000 padding bytes are appended. The gradient-based tactic far surpasses random byte injection, which proves ineffective at similar modification levels. The authors document that the distribution of padding bytes chosen via gradient optimization presents a consistent structure, which is key to the evasion performance.

Theoretical and Practical Implications

The results of this paper bring to light essential considerations both in terms of practical application and theoretical understanding of machine learning in security contexts. Practically, this research underscores a substantial security vulnerability in malware detectors relying on byte-oriented approaches, illuminating the potential for adversaries to bypass detection with minimal file alteration.

Theoretically, the paper questions the assumption that raw byte-level analysis is robust against adversarial manipulations. It suggests the necessity of precise feature extraction or handling of executable structures to avoid over-reliance on raw byte interpretation which can be misleading. Moreover, the authors hint at the challenge of ensuring that deep learning models learn invariant and meaningful patterns from byte sequences without incorporating explicit knowledge about executable structures.

Future Directions

Future exploration could investigate deeper insights into possible defenses against such evasion tactics. Improved robustness could involve the use of more semantically aware analytical strategies, which go beyond naive byte-sequence evaluations. Additionally, expanding the dataset to include more recent and diverse malware samples may offer a more comprehensive understanding of the evasion potential.

Moreover, refining the methodology to allow byte modifications beyond simple appending would offer a more testable framework against various attack vectors. Moreover, cross-exploration with research on better interpretable machine learning algorithms could foster development toward more resilient malware detection systems.

In summary, this paper elucidates a noteworthy vulnerability within machine learning-based malware detection systems and provides a robust foundation for subsequent inquiries into more defensive methodologies seeking to safeguard these systems against adversarial attacks.