摘要:伴随着工业物联网相关技术的高速发展，后门隐私信息的泄露成为一个重大的挑战，严重威胁着工业控制系统及物联网环境的安全性及稳定性.基于工业物联网环境下后门隐私的数据特征定义若干基本属性，根据静态及动态数据流安全威胁抽取上层语义，并基于多属性决策方法聚合生成静态与动态泄露度，最终结合灰色关联分析计算安全级与安全阈值，以此实现后门隐私信息在静态二进制结构及动态数据流向中的泄露场景感知.实验选择目标环境中27种后门隐私信息进行测试，依次计算并分析基本定义、上层语义及判决语义，通过安全级与安全阈值的比较成功感知多种后门泄露场景.实验还将所做工作与其他相关模型或系统进行对比，验证了所提方法的有效性.



Abstract:Leakage of backdoor privacy has become a major challenge with rapid development of industry Internet of Things (ⅡoT), causing serious threat to security and stability of industrial control system and internet of things. In this paper, some basic attributes are defined based on data feature of backdoor privacy in ⅡoT, upper semantics are extracted based on security threat in static and dynamic data flow, static and dynamic leakage degrees are generated based on multi-attribute decision-making, and finally security level and threshold are computed with grey correlation analysis. As a result, perception for leakage scenarios of backdoor privacy can be accomplished in static binary structure and dynamic data flow. Twenty seven types of backdoor privacy are chosen for testing in target environment to compute and analyze basic definitions, upper semantics and judgment semantics, and successful perception for leakage scenarios is performed via comparison between security level and threshold. In addition, effectiveness of this work is validated through comparison with other models and prototypes.



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