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As the number and complexity of malware attacks continue to increase, thereis an urgent need for effective malware detection systems. While deep learningmodels are effective at detecting malware, they are vulnerable to adversarialattacks. Attacks like this can create malicious files that are resistant todetection, creating a significant cybersecurity risk. Recent research has seenthe development of several adversarial attack and response approaches aiming atstrengthening deep learning models' resilience to such attacks. This surveystudy offers an in-depth look at current research in adversarial attack anddefensive strategies for malware classification in cybersecurity. The methodsare classified into four categories: generative models, feature-basedapproaches, ensemble methods, and hybrid tactics. The article outlinescutting-edge procedures within each area, assessing their benefits anddrawbacks. Each topic presents cutting-edge approaches and explores theiradvantages and disadvantages. In addition, the study discusses the datasets andassessment criteria that are often utilized on this subject. Finally, itidentifies open research difficulties and suggests future study options. Thisdocument is a significant resource for malware categorization and cybersecurity researchers and practitioners.

A Malware Classification Survey on Adversarial Attacks and Defences