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P2 Human-in-the-Loop XAI-enabled Vulnerability Detection, Investigation, and Mitigation

人在回路中的可解释AI赋能漏洞检测、调查与缓解
ASE 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)2021匹配度 3/10 · 该论文关注漏洞检测与人为回路的可解释AI,与程序分析/安全有边缘交集,但未涉及用户核心方向(PL→AI、AI→PL、浮点或贝叶斯程序分析),且仅为概念框架,缺少方法实现与基准评估。引用 16 inbox
Tien N. Nguyen, Raymond Choo
crossref: 2026-09-12

AI 速览

一句话:提出 HXAI-VDIM 概念框架,将安全分析师纳入人机回路,用可解释AI与可视化辅助漏洞检测、调查和缓解。

论文关注技术社会对网络弹性的迫切需求:计算系统、设备和数据持续成为攻击目标,复杂安全漏洞的检测、调查与缓解面临挑战。作者提出概念框架 HXAI-VDIM(Human-in-the-Loop Explainable-AI-Enabled Vulnerability Detection, Investigation, and Mitigation)。其核心不是把漏洞处理当作 AI/ML 模型的单一输出,而是将安全分析师或取证调查员纳入人机回路,利用可解释 AI(XAI)结合人工智能与智能助手(IA),在主动与被动流程中增强人类智能。目标是通过安全可视化构建交互式、迭代式的人机协作回路,让人类智能引导 XAI 系统并生成更精细的解决方案。摘要未报告实现或实验评估,属于概念性框架。

问题
在技术社会对网络弹性需求日益增加的背景下,计算系统、设备和数据持续受攻击,复杂安全漏洞的检测、调查与缓解成为难题。
方法
提出 HXAI-VDIM 概念框架,将安全分析师或取证调查员置于人机回路,结合可解释 AI 与智能助手,在主动和被动流程中增强人类智能,并通过安全可视化支持交互式迭代协作。
评估
论文为概念性框架,摘要未给出实现、实验或实证评估;评估部分以框架目标阐述为主。
原文摘要(Abstract)

The need for cyber resilience is increasingly important in our technology-dependent society, where computing systems, devices and data will continue to be the target of cyber attackers. Hence, we propose a conceptual framework called ‘Human-in-the-Loop Explainable-AI-Enabled Vulnerability Detection, Investigation, and Mitigation’ (HXAI-VDIM). Specifically, instead of resolving complex scenario of security vulnerabilities as an output of an AI/ML model, we integrate the security analyst or forensic investigator into the man-machine loop and leverage explainable AI (XAI) to combine both AI and Intelligence Assistant (IA) to amplify human intelligence in both proactive and reactive processes. Our goal is that HXAI-VDIM integrates human and machine in an interactive and iterative loop with security visualization that utilizes human intelligence to guide the XAI-enabled system and generate refined solutions.

链接与引用

DOI 原文 ·

BibTeX
@inproceedings{paperbot6542,
  title = {Human-in-the-Loop XAI-enabled Vulnerability Detection, Investigation, and Mitigation},
  author = {Tien N. Nguyen and Raymond Choo},
  booktitle = {ASE},
  year = {2021},
  doi = {10.1109/ase51524.2021.9678840}
}