Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles

• URL: http://arxiv.org/abs/2405.02731v1
• Date: Sat, 4 May 2024 18:31:38 GMT
• Title: Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles
• Authors: J. R. V. Solaas, N. Tuptuk, E. Mariconti,
• Abstract summary: This systematic review focuses on anomaly detection for connected and autonomous vehicles. The most commonly used Artificial Intelligence (AI) algorithms employed in anomaly detection are neural networks like LSTM, CNN, and autoencoders, alongside one-class SVM. There is a need for future research to investigate the deployment of anomaly detection to a vehicle to assess its performance on the road.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This systematic review focuses on anomaly detection for connected and autonomous vehicles. The initial database search identified 2160 articles, of which 203 were included in this review after rigorous screening and assessment. This study revealed that the most commonly used Artificial Intelligence (AI) algorithms employed in anomaly detection are neural networks like LSTM, CNN, and autoencoders, alongside one-class SVM. Most anomaly-based models were trained using real-world operational vehicle data, although anomalies, such as attacks and faults, were often injected artificially into the datasets. These models were evaluated mostly using five key evaluation metrics: recall, accuracy, precision, F1-score, and false positive rate. The most frequently used selection of evaluation metrics used for anomaly detection models were accuracy, precision, recall, and F1-score. This systematic review presents several recommendations. First, there is a need to incorporate multiple evaluation metrics to provide a comprehensive assessment of the anomaly detection models. Second, only a small proportion of the studies have made their models open source, indicating a need to share models publicly to facilitate collaboration within the research community, and to validate and compare findings effectively. Third, there is a need for benchmarking datasets with predefined anomalies or cyberattacks to test and improve the effectiveness of the proposed anomaly-based detection models. Furthermore, there is a need for future research to investigate the deployment of anomaly detection to a vehicle to assess its performance on the road. There is a notable lack of research done on intrusion detection systems using different protocols to CAN, such as Ethernet and FlexRay.

Related papers

• Can I trust my anomaly detection system? A case study based on explainable AI [0.4416503115535552]
  This case study explores the robustness of an anomaly detection system based on variational autoencoder generative models. The goal is to get a different perspective on the real performances of anomaly detectors that use reconstruction differences.
  arXiv  Detail & Related papers  (2024-07-29T12:39:07Z)
• Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly [4.387337528923525]
  Anomaly detection deals with detecting deviations from established patterns within data. We propose a novel framework for online-adaptive anomaly detection using transfer learning. Experimental results showcase a detection accuracy exceeding 0.975, outperforming the state-of-the-art ET-NET approach.
  arXiv  Detail & Related papers  (2024-06-18T15:11:44Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Diagnosing Human-object Interaction Detectors [42.283857276076596]
  We introduce a diagnosis toolbox to provide detailed quantitative break-down analysis of HOI detection models. We analyze eight state-of-the-art HOI detection models and provide valuable diagnosis insights to foster future research.
  arXiv  Detail & Related papers  (2023-08-16T17:39:15Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series [7.675917669905486]
  This paper presents a systematic and comprehensive evaluation of unsupervised and semi-supervised deep-learning based methods for anomaly detection and diagnosis. We vary the model and post-processing of model errors, through a grid of 10 models and 4 scoring functions, comparing these variants to state of the art methods. We find that the existing evaluation metrics either do not take events into account, or cannot distinguish between a good detector and trivial detectors.
  arXiv  Detail & Related papers  (2021-09-23T15:14:24Z)
• 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 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)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

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.