Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30621
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: IoT Security Vulnerabilities and Predictive Signal Jamming Attack Analysis in LoRaWAN
Author(s): Ingham, Max
Marchang, Jims
Bhowmik, Deepayan
Contact Email: deepayan.bhowmik@stir.ac.uk
Issue Date: 10-Jan-2020
Citation: Ingham M, Marchang J & Bhowmik D (2020) IoT Security Vulnerabilities and Predictive Signal Jamming Attack Analysis in LoRaWAN. IET Information Security. https://doi.org/10.1049/iet-ifs.2019.0447
Abstract: Internet of Things (IoT) gains popularity in recent times due to its flexibility, usability, diverse applicability and ease of deployment. However, the issues related to security is less explored. The IoT devices are light weight in nature and have low computation power, low battery life and low memory. As incorporating security features are resource expensive, IoT devices are often found to be less protected and in recent times, more IoT devices have been routinely attacked due to high profile security flaws. This paper aims to explore the security vulnerabilities of IoT devices particularly that use Low Power Wide Area Networks (LPWANs). In this work, LoRaWAN based IoT security vulnerabilities are scrutinised and loopholes are identified. An attack was designed and simulated with the use of a predictive model of the device data generation. The paper demonstrated that by predicting the data generation model, jamming attack can be carried out to block devices from sending data successfully. This research will aid in the continual development of any necessary countermeasures and mitigations for LoRaWAN and LPWAN functionality of IoT networks in general.
DOI Link: 10.1049/iet-ifs.2019.0447
Rights: This paper is a postprint of a paper submitted to and accepted for publication in IET Information Security and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.
Notes: Output Status: Forthcoming/Available Online

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