Towards a Unified Trust Framework for Detecting IoT Device Attacks in Smart Homes

H. Alsheakh, Shameek Bhattacharjee
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引用次数: 4

Abstract

Trust in Smart Home (SH) Internet of Things (IoT) technologies is a primary concern for consumers, which is preventing the widespread adoption of smart home services. Additionally, the variety of IoT devices and cyber attacks make it hard to build a generic attack detection framework for smart home IoT devices. In this paper, we present a roadmap towards building a unified approach towards establishing trust scores as an indicator of the security status of an IoT device in a smart home that works across multiple attacks and device types/protocols. Specifically, we first introduce artificial reasoning inspired evidence collection approach by introducing a small set of factors that are affected significantly if a smart home IoT device is under attack. Thereafter, we propose an explainable trust scoring model that maps the device level evidence into trust scores in a way that produces lower trust scores when devices are under attack. Specifically, the trust model involves an Augmented Bayesian Belief based Model embedded with novel non-linear weighing functions; explicitly designed to account for the severity of the attack, probabilistic discounting of parts of the evidence caused by benign changes, thus explaining our success. For evaluation of the framework, we use two real datasets that contain a variety of actual cyber-attacks and benign traffic from seven different smart home IoT devices. Our evaluation seeks to investigate the generality of our framework across multiple datasets, with various classes of IoT devices and cyber attacks.
构建智能家居物联网设备攻击检测的统一信任框架
对智能家居(SH)物联网(IoT)技术的信任是消费者主要关注的问题,这阻碍了智能家居服务的广泛采用。此外,物联网设备和网络攻击的多样性使得为智能家居物联网设备构建通用攻击检测框架变得困难。在本文中,我们提出了构建统一方法的路线图,以建立信任分数作为智能家居中物联网设备安全状态的指标,该指标可跨多种攻击和设备类型/协议工作。具体来说,我们首先引入人工推理启发的证据收集方法,通过引入一小部分因素,如果智能家居物联网设备受到攻击,这些因素会受到显著影响。此后,我们提出了一个可解释的信任评分模型,该模型将设备级证据映射到信任分数中,从而在设备受到攻击时产生较低的信任分数。具体来说,信任模型包含了一个基于增强贝叶斯信念的模型,该模型嵌入了新颖的非线性权重函数;明确地设计来解释攻击的严重性,概率贴现部分由良性变化引起的证据,从而解释我们的成功。为了评估该框架,我们使用了两个真实的数据集,其中包含来自七个不同智能家居物联网设备的各种实际网络攻击和良性流量。我们的评估旨在调查我们的框架在多个数据集、各种类型的物联网设备和网络攻击中的通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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