Inferring User Activities from IoT Device Events in Smart Homes: Challenges and Opportunities

The ubiquitous deployment of IoT devices in smart homes has led to growing research interests in studying the home network traffic for various applications such as network measurements, device profiling, and IoT device event inference. Recent studies have shown that user activities can be inferred f...

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Bibliographic Details
Published in:Proceedings - International Conference on Computer Communications and Networks pp. 1 - 10
Main Authors: Lin, Xuanli, Wan, Yinxin, Xu, Kuai, Wang, Feng, Xue, Guoliang
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2022
Subjects:
challenges and opportunities
device events
Digital forensics
Inference algorithms
Internet of Things
Performance evaluation
Smart homes
user activity inference
ISSN:2637-9430
Online Access:Get full text
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Summary:The ubiquitous deployment of IoT devices in smart homes has led to growing research interests in studying the home network traffic for various applications such as network measurements, device profiling, and IoT device event inference. Recent studies have shown that user activities can be inferred from a home network using extracted device event logs. However, existing solutions for user activity inference such as IoTMosaic and \text{E2AP} have limitations when handling ambiguities caused by device malfunctions. In this paper, we first identify the challenges faced by the existing user activity inference algorithms and the root causes of their poor performances on certain types of inputs. We then show that useful information can still be obtained even in situations where device malfunctions introduce ambiguities in user activity patterns. We achieve so by designing an extension to the existing algorithms. We also apply our extension in a digital forensics application. Our extensive experimental evaluations demonstrate that our solutions can effectively provide insights to user activity inference despite the presence of indistinguishable user activity patterns.
ISSN:2637-9430
DOI:10.1109/ICCCN54977.2022.9868917