Cybersecurity-Focused Anomaly Detection in Connected Autonomous Vehicles Using Machine Learning

• URL: http://arxiv.org/abs/2506.22984v1
• Date: Sat, 28 Jun 2025 19:11:19 GMT
• Title: Cybersecurity-Focused Anomaly Detection in Connected Autonomous Vehicles Using Machine Learning
• Authors: Prathyush Kumar Reddy Lebaku, Lu Gao, Yunpeng Zhang, Zhixia Li, Yongxin Liu, Tanvir Arafin,
• Abstract summary: Anomaly detection in connected autonomous vehicles (CAVs) is crucial for maintaining safe and reliable transportation networks.<n>This study explores an anomaly detection approach by simulating vehicle behavior, generating a dataset that represents typical and atypical vehicular interactions.<n>We utilize machine learning models to effectively identify abnormal driving patterns.
• Score: 4.800738030285873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection in connected autonomous vehicles (CAVs) is crucial for maintaining safe and reliable transportation networks, as CAVs can be susceptible to sensor malfunctions, cyber-attacks, and unexpected environmental disruptions. This study explores an anomaly detection approach by simulating vehicle behavior, generating a dataset that represents typical and atypical vehicular interactions. The dataset includes time-series data of position, speed, and acceleration for multiple connected autonomous vehicles. We utilized machine learning models to effectively identify abnormal driving patterns. First, we applied a stacked Long Short-Term Memory (LSTM) model to capture temporal dependencies and sequence-based anomalies. The stacked LSTM model processed the sequential data to learn standard driving behaviors. Additionally, we deployed a Random Forest model to support anomaly detection by offering ensemble-based predictions, which enhanced model interpretability and performance. The Random Forest model achieved an R2 of 0.9830, MAE of 5.746, and a 95th percentile anomaly threshold of 14.18, while the stacked LSTM model attained an R2 of 0.9998, MAE of 82.425, and a 95th percentile anomaly threshold of 265.63. These results demonstrate the models' effectiveness in accurately predicting vehicle trajectories and detecting anomalies in autonomous driving scenarios.

Related papers

• Explore the Use of Time Series Foundation Model for Car-Following Behavior Analysis [0.0]
  Modeling car-following behavior is essential for traffic simulation, analyzing driving patterns, and understanding complex traffic flows.<n>While machine learning and deep learning methods capture complex patterns, they require large labeled datasets.<n>Foundation models provide a more efficient alternative, pre-trained on vast, diverse time series datasets.
  arXiv  Detail & Related papers  (2025-01-13T03:13:32Z)
• MetaFollower: Adaptable Personalized Autonomous Car Following [63.90050686330677]
  We propose an adaptable personalized car-following framework - MetaFollower. We first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability.
  arXiv  Detail & Related papers  (2024-06-23T15:30:40Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  We present nuPlan, a real-world motion planning benchmark that captures multi-agent interactions.<n>We learn to model such unique behaviors with BehaviorNet, a graph convolutional neural network (GCNN)<n>We also present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• Robustness Benchmark of Road User Trajectory Prediction Models for Automated Driving [0.0]
  We benchmark machine learning models against perturbations that simulate functional insufficiencies observed during model deployment in a vehicle. Training the models with similar perturbations effectively reduces performance degradation, with error increases of up to +87.5%. We argue that despite being an effective mitigation strategy, data augmentation through perturbations during training does not guarantee robustness towards unforeseen perturbations.
  arXiv  Detail & Related papers  (2023-04-04T15:47:42Z)
• Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification [7.413735713939367]
  This paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Our objective is to develop an end-to-end pipeline for frame-by-frame annotation of naturalistic driving studies videos.
  arXiv  Detail & Related papers  (2022-11-10T03:38:50Z)
• Anomaly Detection in Multi-Agent Trajectories for Automated Driving [2.5211566369910967]
  Similar to humans, automated vehicles are supposed to perform anomaly detection. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents.
  arXiv  Detail & Related papers  (2021-10-15T08:07:31Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN [59.57221522897815]
  We propose a neural network model based on trajectories information for driving behavior recognition. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.
  arXiv  Detail & Related papers  (2021-03-01T06:47:29Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection [4.251384905163326]
  This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles. Time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models. An initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed.
  arXiv  Detail & Related papers  (2020-10-29T09:29:47Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)

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.