Robust Anomaly Detection via Radio Fingerprinting in LoRa-Enabled IIoT

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Long Range (LoRa) communications are gaining popularity in the Industrial Internet of Things (IIoT) domain due to their large coverage and high energy efficiency. However, LoRa-enabled IIoT networks are susceptible to cyberattacks mainly due to their wide transmission window and freely operated frequency band. This has led to several categories of cyberattacks. However, existing intrusion detection systems are inefficient in detecting compromised device due to the dense deployment and heterogeneous devices. This work introduces Hawk, a distributed anomaly detection system for detecting compromised devices in LoRa-enabled IIoT. Hawk first measures a device-type specific physical layer feature, Carrier Frequency Offset (CFO) and then leverages the CFO for fingerprinting the device and consequently detecting anomalous deviations in the CFO behavior, potentially caused by adversaries. To aggregate the device-type specific CFO behavior profile efficiently, Hawk uses federated learning. To the best of our knowledge, Hawk is the first to use a federated learning method for anomaly-based intrusion detection in LoRa-enabled IIoT. We perform extensive experiments on a real-world dataset collected using 60 LoRa devices, primarily to assess the effectiveness of Hawk against passive attacks. The results show that Hawk improves the detection accuracy by 8% and reduces the storage overhead by 40% than the state-of-the-art solutions.

Original languageEnglish
Title of host publicationInformation Security Practice and Experience - 17th International Conference, ISPEC 2022, Proceedings
EditorsChunhua Su, Dimitris Gritzalis, Vincenzo Piuri
PublisherSpringer Science and Business Media Deutschland GmbH
Pages161-178
Number of pages18
ISBN (Print)9783031212796
DOIs
Publication statusPublished - 2022
Event17th International Conference on Information Security Practice and Experience, ISPEC 2022 - Taipei, Taiwan, Province of China
Duration: 23 Nov 202225 Nov 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13620 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Information Security Practice and Experience, ISPEC 2022
Country/TerritoryTaiwan, Province of China
CityTaipei
Period23/11/2225/11/22

Keywords

  • Anomaly detection
  • Carrier frequency offset
  • Federated learning
  • Industrial IoT
  • LoRa communication

Fingerprint

Dive into the research topics of 'Robust Anomaly Detection via Radio Fingerprinting in LoRa-Enabled IIoT'. Together they form a unique fingerprint.

Cite this