Malware classification in dynamic environments presents a significant challenge due to concept drift, where the statistical properties of malware data evolve over time, complicating detection efforts. To address this issue, we propose a deep learning framework enhanced with a genetic algorithm to improve malware classification accuracy and adaptability. Our approach incorporates mutation operations and fitness score evaluations within genetic algorithms to continuously refine the deep learning model, ensuring robustness against evolving malware threats. Experimental results demonstrate that this hybrid method significantly enhances classification performance and adaptability, outperforming traditional static models. Our proposed approach offers a promising solution for real-time malware classification in ever-changing cybersecurity landscapes.

翻译：动态环境中的恶意软件分类面临概念漂移带来的重大挑战——恶意软件数据的统计特性随时间演变，使检测工作复杂化。为解决此问题，我们提出一种通过遗传算法增强的深度学习框架，以提升恶意软件分类的准确性与适应性。该方法在遗传算法中引入变异操作与适应度评分评估机制，持续优化深度学习模型，确保对持续演变的恶意软件威胁具有鲁棒性。实验结果表明，该混合方法显著提升了分类性能与适应能力，其表现优于传统静态模型。我们提出的方法为持续演变的网络安全环境中的实时恶意软件分类提供了具有前景的解决方案。