The Project discusses the use of deep learning schemes for spoofing detection. Particularly, the characteristics of the so-called Cross Ambiguity Function (CAF) in the presence and absence of spoofing signals are exploited to train a set of data-driven models providing a probabilistic classification. The method operates on a per-satellite basis. The results show that complex neural networks are effectively able to capture the nature of spoofing attacks. Particularly, a Multi-Layer Perceptron (MLP) and two classes of Convolution Neural Networks (CNNs) are considered in this work, validated over simulated data.
2021
Deep Neural Network Approach to Detect GNSS Spoofing Attacks
Presenter: Parisa Borhani Darian
Research Category: Engineering and Technology
College: College of Engineering
Major(s): Computer Engineering
Graduation Date: 2022