通信技术

传统沙箱在应对高级持续性威胁（APT）攻击时存在局限，主要体现在代理指纹易被识别、虚拟化特征明显，以及难以捕获内核级行为。基于此，提出了一种基于全系统虚拟机模拟器QEMU、虚拟机内省（VMI）与动态运行时分析框架（DRAKVUF）的无代理动态分析沙箱系统设计。该设计方案在虚拟机监控器（hypervisor）层构建外部监控架构，利用硬件虚拟化扩展实现系统调用与内存事件的透明截获。系统融合了无代理注入、语义重建及多维抗规避策略，显著提升了环境隐蔽性与行为可见性，在对抗性样本检出率和行为触发效率方面均优于主流开源沙箱。

Conventional sandbox systems face inherent limitations when analyzing APT(advanced persistent threat), as agent-based fingerprints are easily identified, virtualization artifacts are readily detectable, and kernel-level behaviors remain difficult to capture. To address these challenges, this paper proposes an agentless dynamic analysis sandbox based on QEMU(quick emulator), VMI(virtual machine introspection), and DRAKVUF(dynamic runtime analysis kernel user-mode framework). The system establishes an external monitoring architecture at the hypervisor layer, leveraging hardware-assisted virtualization extensions to transparently intercept system calls and memory events. Core mechanisms integrate agentless sample injection, semantic reconstruction, and multi-dimensional anti-evasion strategies, thereby enhancing environmental stealth and behavioral visibility. It outperforms mainstream open-source sandboxes in terms of detection rate of adversarial samples and efficiency of behavior triggering.