Anomaly Detection of Attempt Through Genetic Algorithm and ANN

DOI

10.2991/978-94-6463-136-4_30How to use a DOI?

Keywords

VANET; Security; Algorithm; IDS; Machine learning; Genetic Algorithm; ANN

Abstract

Vehicular ad-hoc network, commonly known as VANET, is an enabling technology for supplying security and useful information in modern transport systems but subject to a multitude of attacks, ranging from auditing passively to hostile interfering. When suspicious actions are discovered, intrusion detection systems (IDS) are essential instruments for risk reduction. Additionally, by sharing interactions among their nodes, VANET vehicle collaborations improve detection accuracy. Because of this, the machine learning distribution system is efficient, scalable, and useful for developing cooperative detection methods over VANETs. Because data is exchanged between nodes during collaborative learning, privacy concerns are a basic barrier. Through the data that is observed, a rogue node may be able to obtain sensitive information about nodes other than itself. This research suggests cooperative IDS for VANETs that protects machine learning privacy. Additionally, an intrusion detection classifier is trained on the VANET and the proposed alternating multiplier direction approach is employed to solve a class of empirical risk minimization issues. In order to apply a vector approach of dual disturbance to dynamically varying privacy and provide secure network communication, the usage of privacy differential is done to capture the notation of privacy.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Suhas Chavan
AU  - N. Jagadisha
AU  - Parikshit Mahalle
AU  - Vinod Kimbahune
PY  - 2023
DA  - 2023/05/01
TI  - Anomaly Detection of Attempt Through Genetic Algorithm and ANN
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 355
EP  - 365
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_30
DO  - 10.2991/978-94-6463-136-4_30
ID  - Chavan2023
ER  -