用生物与环境数据预测人为失误，提前防范网络攻击
BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents
- 融合CNN与LSTM分析传感器数据，识别风险行为模式
- 模型准确率达84%，可有效检测高危人因风险状态
- 适合安全监控、人因工程等需预防误操作的场景
现代组织面临的网络攻击越来越多源于人为行为而非技术故障。为此，我们提出一种以人为本的安全框架，结合卷积神经网络-长短期记忆（CNN-LSTM）模型，分析生物特征与环境数据，实现上下文感知的安全决策。CNN从传感器数据中提取空间模式，LSTM捕捉与人为失误易感性相关的时序动态。该模型达到84%的准确率，证明其能可靠识别导致人为驱动网络安全风险升高的状态。通过持续监测与自适应防护机制，框架支持主动干预，显著降低人为因素引发的网络事件发生概率。
原文摘要 · Abstract (English)
Modern organizations increasingly face cybersecurity incidents driven by human behaviour rather than technical failures. To address this, we propose a conceptual security framework that integrates a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to analyze biometric and environmental data for context-aware security decisions. The CNN extracts spatial patterns from sensor data, while the LSTM captures temporal dynamics associated with human error susceptibility. The model achieves 84% accuracy, demonstrating its ability to reliably detect conditions that lead to elevated human-centred cyber risk. By enabling continuous monitoring and adaptive safeguards, the framework supports proactive interventions that reduce the likelihood of human-driven cyber incidents
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