MPInspector: A Systematic and Automatic Approach for Evaluating the Security of IoT Messaging Protocols

Qinying Wang, S. Ji, Yuan Tian, Xuhong Zhang, Binbin Zhao, Yu Kan, Zhaowei Lin, Changting Lin, Shuiguang Deng, A. Liu, R. Beyah
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引用次数: 19

Abstract

Facilitated by messaging protocols (MP), many home devices are connected to the Internet, bringing convenience and accessibility to customers. However, most deployed MPs on IoT platforms are fragmented and are not implemented carefully to support secure communication. To the best of our knowledge, there is no systematic solution to perform automatic security checks on MP implementations yet. To bridge the gap, we present MPInspector, the first automatic and systematic solution for vetting the security of MP implementations. MPInspector combines model learning with formal analysis and operates in three stages: (a) using parameter semantics extraction and interaction logic extraction to automatically infer the state machine of an MP implementation, (b) generating security properties based on meta properties and the state machine, and (c) applying automatic property based formal verification to identify property violations. We evaluate MPInspector on three popular MPs, including MQTT, CoAP and AMQP, implemented on nine leading IoT platforms. It identifies 252 property violations, leveraging which we further identify eleven types of attacks under two realistic attack scenarios. In addition, we demonstrate that MPInspector is lightweight (the average overhead of end-to-end analysis is ~4.5 hours) and effective with a precision of 100% in identifying property violations.
MPInspector:一种评估物联网消息协议安全性的系统和自动方法
在消息传递协议(MP)的推动下,许多家庭设备连接到互联网,为客户带来便利和可访问性。然而,在物联网平台上部署的大多数MPs都是分散的,并且没有仔细实施以支持安全通信。据我们所知,目前还没有对MP实现执行自动安全检查的系统解决方案。为了弥补这一差距,我们提出了MPInspector,这是第一个用于审查MP实现安全性的自动和系统解决方案。MPInspector将模型学习与形式分析相结合,分三个阶段运行:(a)使用参数语义提取和交互逻辑提取来自动推断MP实现的状态机,(b)基于元属性和状态机生成安全属性,以及(c)应用基于属性的自动形式验证来识别属性违规。我们在九个领先的物联网平台上实施了三种流行的mp,包括MQTT, CoAP和AMQP,并对MPInspector进行了评估。它确定了252个财产违规,利用这些违规行为,我们进一步确定了两种现实攻击场景下的11种攻击类型。此外,我们还证明了MPInspector是轻量级的(端到端分析的平均开销约为4.5小时),并且在识别财产侵犯方面具有100%的精度。
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