Vol. 1 No. 1 (2021): Journal of AI-Assisted Scientific Discovery
Articles

Adaptive Intrusion Detection Systems for Cybersecurity in Autonomous Vehicle Ecosystems

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  • Dr. Andreas Papadopoulos
Dr. Andreas Papadopoulos
Associate Professor of Electrical and Computer Engineering, National Technical University of Athens, Greece
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Published 30-06-2021

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Andreas Papadopoulos, “Adaptive Intrusion Detection Systems for Cybersecurity in Autonomous Vehicle Ecosystems”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 50–71, Jun. 2021, Accessed: Sep. 18, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/59

Abstract

The proposed adaptive system performance analysis is graduated by simulations and does not employ a hard threshold, attacking and harmful instances simultaneously. Additionally, our intrusion system has low complexity and low consumption of memory. This is advantageous in supporting efficient hardware implementation in autonomous vehicle ecosystems.

Layer four adapts the cost of novel class with a threshold learning cost-sensitive model.

Layer three optimizes the deep learning algorithm with the self-developed S2J surrogate model. The training dataset required for Layer three is generated by our proposed intruder-registration wireless intrusion system.

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