AI 中文总结
针对连续多模态认证中现有技术在低信号强度下难以区分真实用户与攻击者的问题，提出VIGIL框架，通过可配置跨模态融合、改进时间融合及采用三区验证决策模型等方法，有效解决现有局限并减少入侵检测时间。
AI 中文摘要
连续多模态认证已成为保护现代环境免受持续威胁的必要手段。现有时间融合技术在信号强度差时难以区分真实用户和持续攻击者。本研究提出VIGIL，一个高度自适应的连续认证框架。引入可配置的跨模态融合及模态加权，用双状态状态转移机改进时间融合，采用三区验证决策模型和自适应收缩验证窗口。实验表明该框架有效解决现有方法局限，减少入侵检测时间并保持对合法用户的高可用性。
英文摘要
Continuous multi-modal authentication has emerged as a necessity for securing modern environments against persistent threats. Existing temporal fusion techniques fail to identify a persistent attacker from a genuine user with poor signal strength. In this study, we propose VIGIL (Verifying Identity via Gated Intermittent Likelihoods for Continuous Biometric Authentication), a highly adaptive continuous authentication framework. We introduce configurable cross-modal fusion with per-modality weighting, enabling operators to select their choice of integration strategy. We improve temporal fusion using dual-state State Transition Machines (STM) with unidirectional transition matrices. A three-zone verification decision model that enables multi-round verification when evidence is inconclusive is used in combination with an adaptive shrinking verification window. Monotonic decay, backflow elimination and analytical evaluation demonstrate that the proposed framework effectively addresses the limitations of existing approaches and reduces the time to detect intrusions while maintaining high usability for legitimate users.
Comments16 pages, 6 figures, Supplementary Material Included
