TY - GEN
T1 - Inferring User Activities from IoT Device Events in Smart Homes
T2 - 31st International Conference on Computer Communications and Networks, ICCCN 2022
AU - Lin, Xuanli
AU - Wan, Yinxin
AU - Xu, Kuai
AU - Wang, Feng
AU - Xue, Guoliang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 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.
AB - 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 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.
KW - Internet-of-things
KW - challenges and opportunities
KW - device events
KW - user activity inference
UR - http://www.scopus.com/inward/record.url?scp=85138385711&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138385711&partnerID=8YFLogxK
U2 - 10.1109/ICCCN54977.2022.9868917
DO - 10.1109/ICCCN54977.2022.9868917
M3 - Conference contribution
AN - SCOPUS:85138385711
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2022 - 31st International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 July 2022 through 27 July 2022
ER -