Affiliation:
1. Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Abstract
In the context of the Internet of Things (IoT), location-based applications have introduced new challenges in terms of location spoofing. With an open and shared wireless medium, a malicious spoofer can impersonate active devices, gain access to the wireless channel, as well as emit or inject signals to mislead IoT nodes and compromise the detection of their location. To address the threat posed by malicious location spoofing attacks, we develop a neural network-based model with single access point (AP) detection capability. In this study, we propose a method for spoofing signal detection and localization by leveraging a feature extraction technique based on a single AP. A neural network model is used to detect the presence of a spoofed unmanned aerial vehicle (UAV) and estimate its time of arrival (ToA). We also introduce a centralized approach to data collection and localization. To evaluate the effectiveness of detection and ToA prediction, multi-layer perceptron (MLP) and long short-term memory (LSTM) neural network models are compared.
Funder
Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference40 articles.
1. Internet of things (IoT), applications and challenges: A comprehensive review;Khanna;Wirel. Pers. Commun.,2020
2. Robustness, security and privacy in location-based services for future IoT: A survey;Chen;IEEE Access,2017
3. Challenges and opportunities in IoT healthcare systems: A systematic review;Selvaraj;SN Appl. Sci.,2020
4. Ullo, S.L., and Sinha, G.R. (2021). Advances in IoT and smart sensors for remote sensing and agriculture applications. Remote Sens., 13.
5. From pre-quantum to post-quantum IoT security: A survey on quantum-resistant cryptosystems for the Internet of Things;IEEE Internet Things J.,2019