Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN (1702.05983v1)

Abstract: Machine learning has been used to detect new malware in recent years, while malware authors have strong motivation to attack such algorithms. Malware authors usually have no access to the detailed structures and parameters of the machine learning models used by malware detection systems, and therefore they can only perform black-box attacks. This paper proposes a generative adversarial network (GAN) based algorithm named MalGAN to generate adversarial malware examples, which are able to bypass black-box machine learning based detection models. MalGAN uses a substitute detector to fit the black-box malware detection system. A generative network is trained to minimize the generated adversarial examples' malicious probabilities predicted by the substitute detector. The superiority of MalGAN over traditional gradient based adversarial example generation algorithms is that MalGAN is able to decrease the detection rate to nearly zero and make the retraining based defensive method against adversarial examples hard to work.

• The paper demonstrates that MalGAN significantly reduces malware detector true positive rates using adversarial examples.
• It employs a generator and substitute detector to mimic and attack unknown black-box systems effectively.
• Experiments reveal nearly zero detection rates across multiple classifiers, exposing critical vulnerabilities in detection methods.

Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN

The paper by Hu and Tan proposes a novel approach to generating adversarial malware examples capable of deceiving black-box machine learning-based malware detection models. The methodology introduces MalGAN, a generative adversarial network (GAN) based algorithm, that advances the process of evading detection systems in a manner more effective than traditional gradient-based adversarial methods. This work holds significant implications for the field of cybersecurity and machine learning, particularly in the area of adversarial machine learning.

The introduction outlines the current landscape of machine learning in malware detection, illustrating the focus on improving detection accuracy and other performance metrics. However, the robustness of these systems against adversarial attacks—which have become a significant concern—has not been as thoroughly explored. This paper addresses that gap by presenting an innovative approach to generating adversarial examples using GANs.

MalGAN Architecture

The architecture of MalGAN consists of a generator and a substitute detector, both of which are standard feed-forward neural networks working in conjunction to subvert a black-box malware detector. The black-box detector, unknown in its specific algorithm and parameters to malware authors, serves as the target system. The generator's role is to transform original malware samples into adversarial examples that can bypass the detection mechanisms. Simultaneously, the substitute detector is trained to approximate the black-box detector using feedback obtained through observing detection outcomes of the generator’s adversarial samples.

Key Experimental Results

The experimental setup utilized a comprehensive dataset of programs, distinguishing between benign and malicious samples through API feature extraction. Multiple machine learning classifiers served as black-box detectors, including Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The results indicate that MalGAN is notably effective, reducing detection rates on adversarial examples to nearly zero across various classifiers, both on training and independent test datasets. This significant decline in true positive rates (TPR) demonstrates MalGAN’s proficiency in generating high-quality adversarial samples that evade detection.

Comparing MalGAN's performance to traditional gradient-based adversarial generation algorithms reveals its superior ability to maintain low detection rates even when the underlying models differ. This demonstrates the robust transferability of adversarial samples created by MalGAN, a feat not achieved as effectively by gradient-based methods.

Implications and Future Directions

The implications of this research are substantial. It underscores the vulnerability of existing malware detection algorithms to adversarial attacks, posing challenges for real-world deployment. The dynamic adversarial generation by MalGAN could result in a perpetual cat-and-mouse game between malware creators and detection systems, emphasizing the need for more resilient defensive mechanisms.

The paper suggests that retraining detection models with newly generated adversarial examples may offer a temporary defense. However, the rapid adaptability of MalGAN, which allows it to relearn and respond almost instantly to updated detectors, indicates that this might not be sustainable long-term.

Future research may focus on enhancing the robustness of detection models against such adversarial attacks or developing innovative defensive strategies that can more broadly anticipate and counteract evolving adversarial threats. Additionally, exploring the adaptation of similar adversarial generation techniques for other domains within machine learning could further unveil vulnerabilities and lead to more secure model deployments.

In conclusion, this paper presents a compelling advancement in adversarial machine learning by leveraging GANs for generating malware examples that effectively eviscerate current detection efforts, prompting a reevaluation of defense tactics in the cybersecurity landscape.