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Deep Learning-Based Location Spoofing Attack Detection and Time-of-Arrival Estimation through Power Received in IoT

Waleed Aldosari¹

¹Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This summary is machine-generated.

This study introduces a neural network model to detect and locate spoofed unmanned aerial vehicles (UAVs) in the Internet of Things (IoT). The system uses single access point (AP) detection to counter location spoofing threats.

Keywords:

IoT UAV localization neural network physical security spoofing attack

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Area of Science:

Cybersecurity and Network Engineering
Artificial Intelligence and Machine Learning
Internet of Things (IoT) Security

Background:

Location-based applications in the Internet of Things (IoT) are vulnerable to location spoofing attacks.
Malicious spoofers can impersonate devices, compromise wireless channels, and mislead IoT nodes, impacting location accuracy.
Existing security measures struggle to effectively detect and mitigate sophisticated spoofing techniques.

Purpose of the Study:

To develop a robust neural network-based model for detecting and localizing spoofed unmanned aerial vehicles (UAVs).
To address the challenge of location spoofing in IoT environments using a single access point (AP) detection capability.
To estimate the time of arrival (ToA) of spoofed signals for improved localization accuracy.

Main Methods:

A novel feature extraction technique leveraging a single access point (AP) for spoofing signal detection.
Implementation of a neural network model (MLP and LSTM) for detecting spoofed UAV presence and estimating ToA.
A centralized approach for efficient data collection and localization of spoofed devices.

Main Results:

The proposed neural network model demonstrates effectiveness in detecting spoofed UAVs.
Accurate estimation of Time of Arrival (ToA) for spoofed signals was achieved.
Comparison between Multi-layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models highlights their performance in detection and ToA prediction.

Conclusions:

The developed neural network model offers a promising solution for combating location spoofing in IoT.
Single AP detection combined with advanced machine learning techniques enhances the security of location-based IoT services.
The findings contribute to securing IoT networks against malicious attacks targeting device location.