Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models
Open Access
- 30 April 2025
- journal article
- research article
- Published by Springer Nature in International Journal of Computational Intelligence Systems
- Vol. 18 (1) , 1-22
Abstract
No abstract availableKeywords
This publication has 29 references indexed in Scilit:
- A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles securityAd Hoc Networks, 2024
- Leveraging Artificial Neural Networks and LightGBM for Enhanced Intrusion Detection in Automotive SystemsArabian Journal for Science and Engineering, 2024
- Intelligent Intrusion Detection Framework for Anomaly-Based CAN Bus Network Using Bidirectional Long Short-Term MemoryJournal of The Institution of Engineers (India): Series B, 2024
- A Novel Collaborative SRU Network With Dynamic Behaviour Aggregation, Reduced Communication Overhead and Explainable FeaturesIEEE Journal of Biomedical and Health Informatics, 2024
- Multi-attack and multi-classification intrusion detection for vehicle-mounted networks based on mosaic-coded convolutional neural networkScientific Reports, 2022
- VehicleEIDS: A Novel External Intrusion Detection System Based on Vehicle Voltage SignalsIEEE Internet of Things Journal, 2021
- Cybersecurity for autonomous vehicles: Review of attacks and defenseComputers & Security, 2021
- Application of Controller Area Network (CAN) bus anomaly detection based on time series predictionVehicular Communications, 2021
- Attacks and defences on intelligent connected vehicles: a surveyDigital Communications and Networks, 2020
- LSTM-Based Intrusion Detection System for In-Vehicle Can Bus CommunicationsIEEE Access, 2020