{"title":"嗅嗅:智能家居中实时设备级异常检测","authors":"Haohua Du, Yue Wang, Xiaoya Xu, Mingsheng Liu","doi":"https://dl.acm.org/doi/10.1145/3586073","DOIUrl":null,"url":null,"abstract":"<p>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, <i>correlated graph</i>, 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.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":"43 16","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Niffler: Real-time Device-level Anomalies Detection in Smart Home\",\"authors\":\"Haohua Du, Yue Wang, Xiaoya Xu, Mingsheng Liu\",\"doi\":\"https://dl.acm.org/doi/10.1145/3586073\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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, <i>correlated graph</i>, 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.</p>\",\"PeriodicalId\":50940,\"journal\":{\"name\":\"ACM Transactions on the Web\",\"volume\":\"43 16\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on the Web\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/https://dl.acm.org/doi/10.1145/3586073\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on the Web","FirstCategoryId":"94","ListUrlMain":"https://doi.org/https://dl.acm.org/doi/10.1145/3586073","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Niffler: Real-time Device-level Anomalies Detection in Smart Home
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.
期刊介绍:
Transactions on the Web (TWEB) is a journal publishing refereed articles reporting the results of research on Web content, applications, use, and related enabling technologies. Topics in the scope of TWEB include but are not limited to the following: Browsers and Web Interfaces; Electronic Commerce; Electronic Publishing; Hypertext and Hypermedia; Semantic Web; Web Engineering; Web Services; and Service-Oriented Computing XML.
In addition, papers addressing the intersection of the following broader technologies with the Web are also in scope: Accessibility; Business Services Education; Knowledge Management and Representation; Mobility and pervasive computing; Performance and scalability; Recommender systems; Searching, Indexing, Classification, Retrieval and Querying, Data Mining and Analysis; Security and Privacy; and User Interfaces.
Papers discussing specific Web technologies, applications, content generation and management and use are within scope. Also, papers describing novel applications of the web as well as papers on the underlying technologies are welcome.