最隐蔽者的生存：通过遗传算法演化低熵勒索软件
Survival of~the~Stealthiest: Evolving Low-Entropy Ransomware via~Genetic Algorithms
AI总结：
本研究将勒索软件执行建模为SBSE优化问题，通过GA演化低熵勒索软件，使其能规避行为监控，为自动化软件防御带来新挑战。
AI中文摘要：
传统勒索软件部署通常依赖大规模加密流程，会立即触发现代防御系统的检测。本研究通过将勒索软件执行建模为基于搜索的软件工程（SBSE）优化问题，在密码攻击领域引入了范式转变。该方法解决了现代威胁中观察到的持续时间差距问题，此类攻击旨在潜伏数小时而非数分钟。我们使用遗传算法（GA）在与基线系统活动的统计偏差的硬性约束下优化数据加密，证明演化出的攻击模式可在指纹识别技术下规避行为监控。研究结果表明，基于搜索的方法为生成规避型恶意软件提供了强大框架，凸显了自动化软件防御面临的新兴挑战。
英文摘要：
Traditional ransomware deployment often relies on massive encryption procedure, triggering immediate detection by modern defense systems. This work introduces a paradigm shift in cryptographic attacks by framing ransomware execution as a Search-Based Software Engineering (SBSE) optimization problem. This approach addresses the persistence gap observed in modern threats, where attacks aim to remain undercover for hours rather than minutes. Using a Genetic Algorithm (GA), we optimize data encryption under a hard constraint on the statistical deviation from baseline system activity. We demonstrate that our evolved attack patterns can evade behavioral monitors under fingerprinting techniques. Our results suggest that search-based methods provide a powerful framework for generating evasive malware, highlighting an emerging challenge for automated software defense.
