Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art (2112.12310v5)

Abstract: Malware has been one of the most damaging threats to computers that span across multiple operating systems and various file formats. To defend against ever-increasing and ever-evolving malware, tremendous efforts have been made to propose a variety of malware detection that attempt to effectively and efficiently detect malware so as to mitigate possible damages as early as possible. Recent studies have shown that, on the one hand, existing ML and DL techniques enable superior solutions in detecting newly emerging and previously unseen malware. However, on the other hand, ML and DL models are inherently vulnerable to adversarial attacks in the form of adversarial examples. In this paper, we focus on malware with the file format of portable executable (PE) in the family of Windows operating systems, namely Windows PE malware, as a representative case to study the adversarial attack methods in such adversarial settings. To be specific, we start by first outlining the general learning framework of Windows PE malware detection based on ML/DL and subsequently highlighting three unique challenges of performing adversarial attacks in the context of Windows PE malware. Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection. Finally, we conclude the paper by first presenting other related attacks against Windows PE malware detection beyond the adversarial attacks and then shedding light on future research directions and opportunities. In addition, a curated resource list of adversarial attacks and defenses for Windows PE malware detection is also available at https://github.com/ryderling/adversarial-attacks-and-defenses-for-windows-pe-malware-detection.

• The paper systematically categorizes adversarial attacks on Windows PE malware detection into white-box and black-box approaches.
• It identifies key challenges such as preserving file format, executability, and malicious functionality during adversarial manipulations.
• The paper reviews defense mechanisms like adversarial training and ensemble methods, and outlines future research directions for enhancing robustness.

An In-Depth Survey of Adversarial Attacks on Windows PE Malware Detection

The paper "Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art" offers a comprehensive review of adversarial attacks targeting Windows Portable Executable (PE) malware detection systems. Written by Xiang Ling et al., this paper systematically categorizes various methods of adversarial attacks against ML and deep learning (DL) models used for detecting malicious software in Windows operating systems. It highlights unique challenges associated with adversarial attacks in the field of Windows PE malware and discusses both existing defenses and directions for future research.

Overview of Windows PE Malware

Windows PE format is predominantly utilized in malicious software targeting Windows operating systems, marked by an extensive number of malware threats documented worldwide. This necessitates efficient Windows PE malware detection mechanisms integrating modern ML and DL strategies. The paper underscores the inherent vulnerabilities of ML/DL models to adversarial examples, which adversely influence malware detection efficacy.

Challenges in Adversarial Attacks on PE Malware

The paper identifies three primary challenges in performing adversarial attacks against Windows PE malware detection:

1. Format-preserving: PE malware must comply with strict file format specifications, ensuring that adversarial manipulations do not violate PE file structure.
2. Executability-preserving: The altered PE malware must remain executable, preserving the inherent functionality post-manipulation.
3. Maliciousness-preserving: Beyond executing, the adversarially altered malware should retain its original malicious capabilities to fulfill intended adversarial goals.

These challenges stem from the transition from adversarial attacks initially applied in simpler domains like images to complex, structured data like PE files, making such attacks non-trivial.

Taxonomy of Adversarial Attacks

Adversarial attacks are classified based on the adversary's knowledge into:

• White-box attacks: These assume full knowledge of the target malware detection model, including its internal parameters and training data. Notable approaches include gradient-based methods like FGSM and C&W adapted for malware detectors.
• Black-box attacks: These assume no detailed insight into the target's workings beyond model outputs. Techniques such as reinforcement learning and GANs are utilized to overcome the lack of specific model knowledge.

Defense Mechanisms Against Adversarial Attacks

The paper reviews several defense mechanisms, primarily focusing on adversarial training—a method refining detection models using adversarial examples to enhance robustness. Other strategies discussed include ensemble methods and heuristic checks for potential adversarial inputs.

Implications and Future Directions

This survey highlights the urgency for advancing adversarial defenses and proposes numerous avenues for future exploration:

• Developing certified robust malware detectors with theoretical guarantees against adversarial threats.
• Broadening the scope of adversarial defenses to tackle dynamic analysis-based malware detection.
• Standardizing benchmark platforms for consistent evaluation of adversarial attack and defense methodologies.

Conclusion

The paper serves as a vital resource for researchers and industry practitioners involved in cybersecurity, particularly emphasizing the need for continuous improvement and innovation in adversarial attack methodologies and defenses within the specialized domain of Windows PE malware detection. By addressing the inherent challenges and summarizing state-of-the-art strategies, it paves the way for more robust and resilient malware detection systems in the future.