Deep Learning for In-Vehicle Intrusion Detection System-Reference-Cited by-同舟云学术

Deep Learning for In-Vehicle Intrusion Detection System

Published:2020 Issue: Volume: Page:50-58
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Gherbi Elies,Hanczar Blaise,Janodet Jean-Christophe,Klaudel Witold

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-63820-7_6

Reference19 articles.

1. Avatefipour, O., Malik, H.: State-of-the-art survey on in-vehicle network communication (can-bus) security and vulnerabilities. CoRR (2018)

2. Bagnall, A.J., Bostrom, A., Large, J., Lines, J.: The great time series classification bake off: an experimental evaluation of recently proposed algorithms. extended version. CoRR abs/1602.01711 (2016)

3. Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR abs/1803.01271 (2018)

4. Dupont, G., den Hartog, J., Etalle, S., Lekidis, A.: Network intrusion detection systems for in-vehicle network - technical report. CoRR (2019)

5. Seo, E., Song, H.M., Kim, H.K.: Gids: gan based intrusion detection system for in-vehicle network. In: 2018 16th (PST) (2018)

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1. A Study of AI-Based In-Vehicle Intrusion Detection Systems;2024 IEEE 21st Consumer Communications & Networking Conference (CCNC);2024-01-06

2. AI-Based Intrusion Detection Systems for In-Vehicle Networks: A Survey;ACM Computing Surveys;2023-02-09

3. DAD: A Distributed Anomaly Detection framework for future In-vehicle network;2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME);2022-11-16

4. A Review of Intrusion Detection Systems: Datasets and machine learning methods;The 4th International Conference on Networking, Information Systems amp Security.;2021-04