RF and Network Signature-based Machine Learning on Detection of Wireless Controlled Drone

Abstract

Over the years, drone usage have become an increasing part of the ever-connected society that we are currently living in. Its usages have proliferated beyond the military sector to various commercial and consumer activities such as package delivery, disaster relief, agriculture and filming. With the rise of Wi-Fi controlled drone. Wi-Fi controlled drone has increased its popularity for personal use due to its affordability, and the ease of operating the drone through smart-devices like mobile phone, tablets and computers. As such, this increases the likelihood of drone presence in various environments, especially in critical government infrastructure, leading to various privacy and security concern by the authorities and the public with malicious intent. Therefore, various signature-based methodology of drone detection has emerged such as the visual and Radio Frequency (RF) signature-based detection method. Visual signature-based detection relies on camera capture and image processing but this is an expensive approach. Whereas, RF signature- based detection relies on the identification of the emission of RF signal by the drone. However, since most commercial electronics devices were built based on Wi-Fi technology, the differentiation of the RF signals transmitted between a drone or a standard Wi-Fi device in a crowded Wi-Fi environment such as a school campus or city area is an challenging task. In this paper, we propose a novel machine learning approach that leverages on the identified unique signatures of Wi-Fi devices in terms of Radio Frequency (RF) and network packets measurement to differentiate the presence of Wi-Fi drone and standard Wi-Fi devices in an urban setting. Furthermore, we also carried out a meticulous pre-processing procedure and a better training scheme of using Stratified K-Fold Cross-Validation (SKFCV), to enhance the richness in the data signature and fully exploit the permutation of the data during training respectively for better performance of the ML models. Two supervised classification Machine Learning (ML) models, namely the Logistic Regression (LR), and Artificial Neural Network (ANN) were applied using the joint data measurements to identify the presence of drone in dense Wi-Fi environment. The experimental results have shown that the proposed novel ML approach of using both RF and network measurement signatures coupled with the pre-processing and training methodology on LR and ANN ML models have outperformed the traditional RF signature-based drone detection ML accuracy results by 15.1% and 21.63% respectively in a crowded Wi-Fi environment.