基于API调用序列分析与概念漂移处理的深度学习驱动恶意软件分类
Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling
AI总结：
本文针对恶意软件分类中的概念漂移问题，提出遗传算法增强的深度学习框架，通过变异操作和适应度评估持续优化模型，实验证明其分类性能和适应性优于传统静态模型。
摘要：
动态环境中的恶意软件分类因概念漂移而面临重大挑战，即恶意软件数据的统计特性随时间演变，使检测工作复杂化。为解决这一问题，我们提出了一种结合遗传算法增强的深度学习框架，以提高恶意软件分类的准确性和适应性。我们的方法在遗传算法中引入变异操作和适应度评分评估，以持续优化深度学习模型，确保其对不断演变的恶意软件威胁具有鲁棒性。实验结果表明，这种混合方法显著提升了分类性能和适应性，优于传统的静态模型。我们提出的方法为在不断变化的网络安全环境中进行实时恶意软件分类提供了一种有前景的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
