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Detection of abnormal driving situations using distributed representations and unsupervised learning
2020
The European Symposium on Artificial Neural Networks
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In this paper, we present an anomaly detection system employing an unsupervised learning model trained on the information encapsulated within distributed vector representations of automotive scenes. ...
We train a neural network autoencoder in unsupervised fashion to detect anomalies based on this representation. ...
We observe a similar distribution for the distance between the target and the ego-vehicle, where the distances are more or less equally distributed around the mean of 40 m for the complete data set. ...
dblp:conf/esann/MirusSC20
fatcat:wvoyqcbxe5cxrpqbl6f2ukzkkm
Advanced Analytics for Connected Cars Cyber Security
[article]
2017
arXiv
pre-print
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We present a new approach for detecting anomalies, tailored to the temporal nature of our domain. ...
likelihood threshold for anomaly. ...
Datasets In order to collect large amount of data and simulate car fleets, we have built a small simulation for connected vehicles. ...
arXiv:1711.01939v2
fatcat:gyfxqzumsjbzxkhirpmzyaftxe
A Distributed Anomaly Detection System for In-Vehicle Network Using HTM
2018
IEEE Access
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2018) A distributed anomaly detection system for in-vehicle network using HTM. IEEE Access, 6 . pp. 9091-9098. ISSN 2169-3536 (Published online first) Published version (with publisher's formatting) ...
CONCLUSION In this paper, a distributed anomaly detection system based on HTM learning algorithm is introduced, which is used to detect the data sequence anomaly of the vehicle CAN bus network. ...
We have made the following contributions: 1) A new distributed vehicle network anomaly detection framework has been proposed. ...
doi:10.1109/access.2018.2799210
fatcat:diyzobzhsvecdgtotipehdsau4
Anomaly Detection in the Internet of Vehicular Networks Using Explainable Neural Networks (xNN)
2022
Mathematics
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After extracting the best features for anomaly detection, we applied a novel model, i.e., an Explainable Neural Network (xNN), to classify attacks in the CICIDS2019 dataset and UNSW-NB15 dataset separately ...
For the last few years, detecting attacks in the IoV has been a challenging task. ...
[14] proposed a framework for a distributed anomaly detection system that incorporates an online new data selection algorithm that directs retraining and modifies the model parameters as needed for ...
doi:10.3390/math10081267
fatcat:ven2idnwvjgvnl7qw6gydslpfa
Online Encoder-decoder Anomaly Detection using Encoder-decoder Architecture with Novel Self-configuring Neural Networks & Pure Linear Genetic Programming for Embedded Systems
2019
International Joint Conference on Computational Intelligence
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This paper presents two related new methods for anomaly detection within data sets gathered from an autonomous mini-vehicle with a CAN bus. ...
The first method which to the best of our knowledge is the first use of encoder-decoder architecture for anomaly detection using linear genetic programming (LGP). ...
Collection of Data The data used for training and testing of the proposed anomaly detection model was gathered from a selfdriving mini-vehicle. ...
doi:10.5220/0008064401630171
dblp:conf/ijcci/KasparaviciuteT19
fatcat:ieyowmk2fbesbijchcegsaaa5e
CAD3: Edge-facilitated Real-time Collaborative Abnormal Driving Distributed Detection
2021
2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)
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For a definitive version of this work, please refer to the published version. ...
We create three data topics, i.e., "IN-DATA" for ingesting the incoming vehicular data, "OUT-DATA" for writing detected anomalies, and "CO-DATA" for writing detection summaries. ...
[3] . • Continuous Big Data Stream: Connected vehicles continuously generate massive amounts of data. ...
doi:10.1109/icdcs51616.2021.00074
fatcat:fmz7wwcaxrhdtebun3mxws76yi
Anomaly Detection Methodology of In-vehicle Network Based on Graph Pattern Matching
2022
Computer and Information Science
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We have validated this anomaly detection methodology on public datasets and in an actual vehicle environment. ...
Vehicles are becoming more and more connected today, with many direct interfaces and infotainment units widely deployed in in-vehicle networks. ...
for detecting an anomaly. ...
doi:10.5539/cis.v15n2p78
fatcat:bqxiwwvq4rfhvmkqoeizruonbm
Anomaly Detection in Autonomous Driving: A Survey
[article]
2022
arXiv
pre-print
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We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. ...
This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. ...
Acknowledgment This work results from the project KI Data Tooling (19A20001J), funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). ...
arXiv:2204.07974v1
fatcat:3rdola4tjfesllr6dqhqgf3tve
Proposal of Real Time Predictive Maintenance Platform with 3D Printer for Business Vehicles
[article]
2016
arXiv
pre-print
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This paper proposes a maintenance platform for business vehicles which detects failure sign using IoT data on the move, orders to create repair parts by 3D printers and to deliver them to the destination ...
repair parts and also distributes repair parts data to 3D printers to create repair parts near the destination. ...
In cloud side, Jubatus analyzes suspicious data deeply and detects anomaly with high accuracy, and Jubatus updates a learning model by aggregating multiple sites data and distributes it. ...
arXiv:1611.09944v1
fatcat:zy24iq4hxzeqhfsyopzybxsiyi
RSU-Based Online Intrusion Detection and Mitigation for VANET
[article]
2022
arXiv
pre-print
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In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting the integrity and availability, respectively, in ...
Novel statistical intrusion detection and mitigation techniques based on centralized communications through roadside units (RSU) are proposed for the considered attacks. ...
CONCLUSION We proposed a statistical nonparametric intrusion detection system (IDS) for online detection of false data injection (FDI) attacks and distributed denial-of-service (DDoS) attacks. ...
arXiv:2207.10812v1
fatcat:khjuh3qkerckliactpolkpauma
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles
[article]
2021
arXiv
pre-print
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Change point models, can be used for real-time anomaly detection caused by the false information attack. ...
Also, data-driven artificial intelligence (AI) models, which can be used to detect known and unknown underlying patterns in the dataset, have the potential of detecting a real-time anomaly in the CV data ...
Recurrent neural network models are also used for anomaly detection in in-vehicle networks (24) . ...
arXiv:2108.01124v1
fatcat:qgriu57inrfj3diej7qwngxrm4
Proposal of Real Time Predictive Maintenance Platform with 3D Printer for Business Vehicles
2016
International Journal of Information and Electronics Engineering
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This paper proposes a maintenance platform for business vehicles which detects failure sign using IoT data on the move, orders to create repair parts by 3D printers and to deliver them to the destination ...
repair parts and also distributes repair parts data to 3D printers to create repair parts near the destination. ...
In cloud side, Jubatus analyzes suspicious data deeply and detects anomaly with high accuracy, and Jubatus updates a learning model by aggregating multiple sites data and distributes it. ...
doi:10.18178/ijiee.2016.6.5.640
fatcat:ohyf5qyw7jf65narcs6dl4rjd4
Evaluation of the Architecture Alternatives for Real-time Intrusion Detection Systems for Connected Vehicles
[article]
2022
arXiv
pre-print
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The evaluation shows that a real-time IDS for a connected vehicle designed as two processes, a process for CAN Bus monitoring and another one for anomaly detection engine is reliable (no loss of messages ...
This paper evaluates four architecture designs for real-time IDS for connected vehicles using Controller Area Network (CAN) datasets collected from a moving vehicle under malicious speed reading message ...
(b) Impacts of cyber-attacks on connected vehicles
Fig. 2: Growth and distribution of cyber-attacks on connected vehicles between 2010 and 2019 [4]. ...
arXiv:2201.06680v1
fatcat:hkwyesnrqzc7pgvefgom4qecta
Deep Learning in Mobile Computing: Architecture, Applications, and Future Challenges
2021
Mobile Information Systems
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nodes and combines a historical trajectory extraction detection model with an online anomaly detection model to detect anomalies. ...
Online anomaly detection algorithms detect anomalous behavior based on spatiotemporal neighborhood similarity and reduce the impact of anomaly evolution. ...
doi:10.1155/2021/9874724
fatcat:exsfe42mkrfwxnm6erdwl46h5y
Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey
2018
International Journal of Advanced Technology and Engineering Exploration
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attacks like data injection attacks, replay attacks, etc. ...
The different deep learning algorithm based cyber-attack detection schemes have been designed to detect and mitigate the different types of cyber-attacks through CPSs, smart grids, power systems, etc. ...
For instance, water management and distribution plants, power grids and autonomous vehicles. Mostly, these systems are connected to support secluded monitoring and control. ...
doi:10.19101/ijatee.2018.547030
fatcat:ivfb6skvyrcdve6mo5qtrajdty
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