Adaptive Network Segmentation Strategies for Cybersecurity in Autonomous Vehicle Networks
Keywords:
Electronic Control Units, anomaly intrusion detectorsAbstract
Vehicular systems are exposed regularly to new and sophisticated security attacks, aiming to violate users’ privacy, steal drivers’ sensitive data, and compromise vehicular Electronic Control Units. Thus, the improved defense systems for autonomous vehicles need to misuse and anomaly intrusion detectors combined with real-time user behavioral modeling and a robust and elastic network design, capable of adding or removing defensive segments according to the surrounding threat level. All the above-mentioned goals demand the implementation of an intelligent infrastructure that can self-adapt to the new network configuration depending on the similar network threat level, in order to the adaptively optimized network configuration can be obtained. The paper describes the devised methodology and preliminary findings in the design of a self-adaptive network segmentation strategy to protect the vehicular network [1].
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References
Sadhu, Ashok Kumar Reddy, et al. "Enhancing Customer Service Automation and User Satisfaction: An Exploration of AI-powered Chatbot Implementation within Customer Relationship Management Systems." Journal of Computational Intelligence and Robotics 4.1 (2024): 103-123.
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
Perumalsamy, Jegatheeswari, Muthukrishnan Muthusubramanian, and Selvakumar Venkatasubbu. "Actuarial Data Analytics for Life Insurance Product Development: Techniques, Models, and Real-World Applications." Journal of Science & Technology 4.3 (2023): 1-35.
Devan, Munivel, Lavanya Shanmugam, and Manish Tomar. "AI-Powered Data Migration Strategies for Cloud Environments: Techniques, Frameworks, and Real-World Applications." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 79-111.
Selvaraj, Amsa, Chandrashekar Althati, and Jegatheeswari Perumalsamy. "Machine Learning Models for Intelligent Test Data Generation in Financial Technologies: Techniques, Tools, and Case Studies." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 363-397.
Katari, Monish, Selvakumar Venkatasubbu, and Gowrisankar Krishnamoorthy. "Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.3 (2023): 473-495.
Tatineni, Sumanth, and Naga Vikas Chakilam. "Integrating Artificial Intelligence with DevOps for Intelligent Infrastructure Management: Optimizing Resource Allocation and Performance in Cloud-Native Applications." Journal of Bioinformatics and Artificial Intelligence 4.1 (2024): 109-142.
Prakash, Sanjeev, et al. "Achieving regulatory compliance in cloud computing through ML." AIJMR-Advanced International Journal of Multidisciplinary Research 2.2 (2024).
Venkataramanan, Srinivasan, et al. "Leveraging Artificial Intelligence for Enhanced Sales Forecasting Accuracy: A Review of AI-Driven Techniques and Practical Applications in Customer Relationship Management Systems." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 267-287.
Makka, Arpan Khoresh Amit. “Integrating SAP Basis and Security: Enhancing Data Privacy and Communications Network Security”. Asian Journal of Multidisciplinary Research & Review, vol. 1, no. 2, Nov. 2020, pp. 131-69, https://ajmrr.org/journal/article/view/187.
