Niffler: Real-time Device-level Anomalies Detection in Smart Home
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Published:2023-05-22
Issue:3
Volume:17
Page:1-27
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ISSN:1559-1131
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Container-title:ACM Transactions on the Web
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language:en
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Short-container-title:ACM Trans. Web
Author:
Du Haohua1ORCID,
Wang Yue1ORCID,
Xu Xiaoya1ORCID,
Liu Mingsheng2ORCID
Affiliation:
1. Beihang University, China
2. Shijiazhuang Institute of Railway Technology, China
Abstract
Device-level security has become a major concern in smart home systems. Detecting problems in smart home sytems strives to increase accuracy in near real time without hampering the regular tasks of the smart home. The current state of the art in detecting anomalies in smart home devices is mainly focused on the app level, which provides a basic level of security by assuming that the devices are functioning correctly. However, this approach is insufficient for ensuring the overall security of the system, as it overlooks the possibility of anomalies occurring at the lower layers such as the devices. In this article, we propose a novel notion,
correlated graph
, and with the aid of that, we develop our system to detect misbehaving devices without modifying the existing system. Our correlated graphs explicitly represent the contextual correlations among smart devices with little knowledge about the system. We further propose a linkage path model and a sensitivity ranking method to assist in detecting the abnormalities. We implement a semi-automatic prototype of our approach, evaluate it in real-world settings, and demonstrate its efficiency, which achieves an accuracy of around 90% in near real time.
Funder
National Key R&D Program of China
China National Natural Science Foundation
S&T Program of Hebei
Opening Project of Shanghai Trusted Industrial Control Platform
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
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