A holistic IoT device classification approach through spatial & temporal behaviors modelling

Yichiet Aun (), Yen-Min Khaw () and Ming-Lee Gan ()
Additional contact information
Yichiet Aun: Universiti Tunku Abdul Rahman
Yen-Min Khaw: Universiti Tunku Abdul Rahman
Ming-Lee Gan: Universiti Tunku Abdul Rahman

Telecommunication Systems: Modelling, Analysis, Design and Management, 2022, vol. 79, issue 4, No 4, 515-528

Abstract: Abstract Traffic management is becoming increasingly complex due to the increasing diversity of IoT platforms and protocols supported by heterogeneous devices. Recently, device fingerprinting techniques for automatic device recognition leveraging on domain knowledge in TCP/IP and AI techniques are becoming more prevalent. However, existing machine-learning (ML) models have trained HTTP and TCP flows that are not correctly weighted. Besides, these models are trained with algorithms that can extrapolate temporal information; thus, they are not temporal aware. This paper presents a two-level machine learning pipeline (IoT Sense) for IoT device recognition, using (1) SVM and Decision Tree to model the spatial behaviors and (2) RNN to model the device-to-device temporal actions. The ground truth of the work is that the communication behaviors of IoT sensors, actuators, and devices are more deterministic using control plane traffic instead of data plane traffic. IoT devices commonly rely on SSDP, IoTivity, AllJoyn to discover neighbors and exchange devices' information. These device-to-device hello(s) are more predictable than traffic patterns induced by random usage behaviors. Deep learning (DL) models trained on raw traffic; without adding custom weightage to control packets; can be biased towards user-induced behaviors that eventually over-fit the resulting model. IoT Sense classify based on the connectivity pattern among IoT nodes using control plane traffic that includes discovery, handshake, and session establishment flows. IoT Sense is platform-agnostic since it operates on connection properties (TCPIP/layer 3) instead of protocols (like CoAP, MQTT). The experimental results show that the proposed context-aware model achieved accuracy up to 0.956 precision score with a 0.0957 recall rate in IoT devices classification.

Keywords: Device fingerprinting; IoT Discovery; Spatial & temporal aware; Continuous learning (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11235-021-00867-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:79:y:2022:i:4:d:10.1007_s11235-021-00867-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-021-00867-x

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().