  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.
