用遗传算法动态优化深度学习模型，提升病毒分类对新威胁的适应能力。
Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling
- 结合遗传算法的突变与评分机制，持续优化深度学习模型
- 在动态环境中显著提升分类准确率和适应性
- 适合需要实时更新的网络安全检测系统
动态环境中的恶意软件分类面临重大挑战，因恶意软件数据的统计特性随时间演变，导致检测难度增加。为应对这一问题，我们提出一种融合遗传算法的深度学习框架，以提升恶意软件分类的准确性和适应性。该方法在遗传算法中引入突变操作和适应度评分，持续优化深度学习模型，增强其对演化中恶意软件威胁的鲁棒性。实验结果表明，这种混合方法显著提升了分类性能与适应能力，优于传统静态模型。所提方案为不断变化的网络安全环境中实现实时恶意软件分类提供了有前景的解决方案。
原文摘要 · Abstract (English)
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.
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