As the number and complexity of malware attacks continue to increase, there is an urgent need for effective malware detection systems. While deep learning models are effective at detecting malware, they are vulnerable to adversarial attacks. Attacks like this can create malicious files that are resistant to detection, creating a significant cybersecurity risk. Recent research has seen the development of several adversarial attack and response approaches aiming at strengthening deep learning models' resilience to such attacks. This survey study offers an in-depth look at current research in adversarial attack and defensive strategies for malware classification in cybersecurity. The methods are classified into four categories: generative models, feature-based approaches, ensemble methods, and hybrid tactics. The article outlines cutting-edge procedures within each area, assessing their benefits and drawbacks. Each topic presents cutting-edge approaches and explores their advantages and disadvantages. In addition, the study discusses the datasets and assessment criteria that are often utilized on this subject. Finally, it identifies open research difficulties and suggests future study options. This document is a significant resource for malware categorization and cyber security researchers and practitioners.

翻译：随着恶意软件攻击的数量和复杂性持续增长，亟需有效的恶意软件检测系统。尽管深度学习模型在检测恶意软件方面效果显著，但它们容易受到对抗性攻击的影响。此类攻击能够创建逃避检测的恶意文件，构成重大的网络安全风险。近年来，研究已经开发出多种对抗攻击与防御方法，旨在增强深度学习模型对此类攻击的鲁棒性。本综述研究深入探讨了当前网络安全中针对恶意软件分类的对抗攻击与防御策略。这些方法被分为四类：生成模型、基于特征的方法、集成方法和混合策略。本文概述了每个领域中的前沿技术，并评估了其优缺点。每个主题都介绍了前沿方法，并探讨了其优势与局限。此外，本研究还讨论了该领域常用的数据集和评估标准。最后，指出了待解决的开放研究难题，并提出了未来研究方向。本文档为恶意软件分类与网络安全领域的研究人员和实践者提供了重要参考资源。