New capabilities in wireless network security have been enabled by deep learning that leverages and exploits signal patterns and characteristics in Radio Frequency (RF) data captured by radio receivers to identify and authenticate radio transmitters. Open-set detection is an area of deep learning that aims to identify RF data samples captured from new devices during deployment (aka inference) that were not part of the training set; i.e. devices that were unseen during training. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. In this paper, we introduce a novel open-set detection approach for RF data-driven device identification that extracts its neural network features from patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory (CNN+LSTM) model. Experimental results obtained using real datasets collected from 15 IoT devices, each enabled with LoRa, wireless-Wi-Fi, and wired-Wi-Fi communication protocols, show that our new approach greatly improves the area under the precision-recall curve, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.
Poster Abstract: O-RAN Signaling Optimizations for Improved IoT Handover Performance in 5G Networks
IoT systems require a wireless infrastructure that supports 5G devices, including handovers between heterogeneous and/or small cell radio access networks. These networks are subject to increased radio link failures and loss of IoT network function. 3GPP new radio (NR) applications include multihoming, i.e., simultaneously connecting devices, and handover, i.e., changing the point of access to the network. This work leverages the open radio access network (O-RAN) alliance, which specifies a new open architecture with intelligent controllers, to improve handover management. A new feedback-based time-to-trigger (TTT) handover mechanism is introduced. Improved throughput and reduced radio link failures over other techniques were achieved.
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- Award ID(s):
- 2030122
- PAR ID:
- 10490168
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation
- ISBN:
- 9798400700378
- Page Range / eLocation ID:
- 454 to 455
- Format(s):
- Medium: X
- Location:
- San Antonio TX USA
- Sponsoring Org:
- National Science Foundation
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