Leveraging Semantic Technologies for Collaborative Inference of Threatening IoT Dependencies

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Applied Computing Review Pub Date : 2023-09-01 DOI:10.1145/3626307.3626310
Amal Guittoum, François Aïssaoui, Sébastien Bolle, Fabienne Boyer, Noel De Palma
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Abstract

IoT Device Management (DM) refers to the remote administration of customer devices. In practice, DM is ensured by multiple actors such as operators or device manufacturers, each operating independently via their DM solution. These siloed DM solutions are limited in addressing IoT threats related to device dependencies, such as cascading failures, as these threats spread across devices managed by different DM actors, and their mitigation can no longer be performed without collaborative DM efforts. The first step toward collaborative mitigation of these threats is the identification of threatening dependency topology. However, this task is challenging, requiring the inference of dependencies from the data held by different actors. In this work, we propose a collaborative framework that infers the threatening topology of dependencies by accessing and aggregating data from legacy DM solutions. It combines the assets of Semantic Web standards and Digital Twin technology to capture on-demand the topology of dependencies, and it is designed to be used in business applications such as customer care to enhance customer Quality of Experience. We integrate our solution within the in-use Orange's Digital Twin platform Thing in the future and demonstrate its effectiveness by automatically inferring threatening dependencies in the two settings: a simulated smart home scenario managed by ground-truth DM solutions, such as Orange's implementation of the USP Controller and Samsung's SmartThings Platform , and a realistic smart home called DOMUS testbed.
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利用语义技术进行威胁物联网依赖关系的协同推理
物联网设备管理(DM)是指对客户设备的远程管理。实际上,DM由多个参与者(如运营商或设备制造商)确保,每个参与者都通过其DM解决方案独立运行。这些孤立的数据管理解决方案在解决与设备依赖关系相关的物联网威胁(如级联故障)方面受到限制,因为这些威胁在不同数据管理参与者管理的设备之间传播,如果没有数据管理的协作努力,就无法再执行缓解威胁的措施。协作缓解这些威胁的第一步是识别具有威胁的依赖关系拓扑。然而,这项任务具有挑战性,需要从不同参与者持有的数据中推断依赖关系。在这项工作中,我们提出了一个协作框架,通过访问和聚合来自遗留DM解决方案的数据来推断依赖关系的威胁拓扑。它结合了语义Web标准和Digital Twin技术的资产,以按需捕获依赖关系的拓扑,并且它被设计用于业务应用程序,例如客户服务,以提高客户体验质量。我们将我们的解决方案集成到未来正在使用的Orange的数字孪生平台Thing中,并通过自动推断两种设置中的威胁依赖性来证明其有效性:由真实DM解决方案管理的模拟智能家居场景,例如Orange的USP控制器和三星的SmartThings平台的实施,以及一个名为DOMUS的现实智能家居测试平台。
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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