Deep Sequence-to-Sequence Models for GNSS Spoofing Detection

• URL: http://arxiv.org/abs/2510.19890v1
• Date: Wed, 22 Oct 2025 16:53:41 GMT
• Title: Deep Sequence-to-Sequence Models for GNSS Spoofing Detection
• Authors: Jan Zelinka, Oliver Kost, Marek Hrúz,
• Abstract summary: We apply deep neural network-based models for spoofing detection, utilizing Long Short-Term Memory networks and Transformer-inspired architectures.<n>Our results demonstrate that deep learning models can accurately distinguish spoofed signals from genuine ones, achieving high detection performance.
• Score: 1.932919360019125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a data generation framework designed to simulate spoofing attacks and randomly place attack scenarios worldwide. We apply deep neural network-based models for spoofing detection, utilizing Long Short-Term Memory networks and Transformer-inspired architectures. These models are specifically designed for online detection and are trained using the generated dataset. Our results demonstrate that deep learning models can accurately distinguish spoofed signals from genuine ones, achieving high detection performance. The best results are achieved by Transformer-inspired architectures with early fusion of the inputs resulting in an error rate of 0.16%.

Related papers

• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [81.93945602120453]
  We introduce an approach that is both general and parameter-efficient for face forgery detection.<n>We design a forgery-style mixture formulation that augments the diversity of forgery source domains.<n>We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks [3.489779105594534]
  We introduce a novel approach to backdoor detection using two tensor decomposition methods applied to network activations. This has a number of advantages relative to existing detection methods, including the ability to analyze multiple models at the same time. Results show that our method detects backdoored networks more accurately and efficiently than current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-06T03:08:28Z)
• IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection [3.3772986620114387]
  IoTGeM is an approach for modeling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance.<n>We build and test our models using strictly isolated train and test datasets.<n> IoTGeM achieves F1 scores of 99% for ACK, HTTP, SYN, MHD, and PS attacks, as well as a 94% F1 score for UDP attacks.
  arXiv  Detail & Related papers  (2023-10-17T21:46:43Z)
• Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards [68.95806641664713]
  We explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection. We train a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images.
  arXiv  Detail & Related papers  (2023-06-20T09:46:01Z)
• Anomaly Detection with Ensemble of Encoder and Decoder [2.8199078343161266]
  Anomaly detection in power grids aims to detect and discriminate anomalies caused by cyber attacks against the power system. We propose a novel anomaly detection method by modeling the data distribution of normal samples via multiple encoders and decoders. Experiment results on network intrusion and power system datasets demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-03-11T15:49:29Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks [2.3204135551124407]
  Intrusion Detection Systems are widely used to detect cyberattacks. We present a deep learning-based sequential model for offline intrusion detection on SOME/IP protocol.
  arXiv  Detail & Related papers  (2021-08-04T09:58:06Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Deep Learning based Covert Attack Identification for Industrial Control Systems [5.299113288020827]
  We develop a data-driven framework that can be used to detect, diagnose, and localize a type of cyberattack called covert attacks on smart grids. The framework has a hybrid design that combines an autoencoder, a recurrent neural network (RNN) with a Long-Short-Term-Memory layer, and a Deep Neural Network (DNN)
  arXiv  Detail & Related papers  (2020-09-25T17:48:43Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.