پذیرفته شده برای ارائه شفاهی ، صفحه 85-100 (16)

کد مقاله : 1119-CYSP2024 (R1)

¹PhD Student at School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran

²Undergraduate Student in Computer Engineering at Faculty of Engineering, College of Farabi, University of Tehran, Iran

³Associate Professor at School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran

In the context of smart cities, real-time route planning systems are essential for both autonomous and conventional vehicles. However, the reliance on Advanced Driver Assistance Systems (ADAS) introduces cybersecurity vulnerabilities. This paper proposes a framework using Quantum Neural Networks (QNNs) to address these issues by combining quantum computing's data processing capabilities with neural networks' decision-making strengths. The framework incorporates real-time threat detection using quantum parallelism and neural network pattern recognition to identify and mitigate cyberattacks at an early stage. Quantum algorithms, such as Grover’s and Shor’s, are utilized to optimize search processes and secure communications. QNNs enable dynamic feedback, refining decision-making to adapt to evolving threats while maintaining computational efficiency.The integration of QNNs enhances route planning and protects transportation systems against emerging cyber threats, contributing to improved operational efficiency and cybersecurity resilience in smart cities.

Cyberattack؛ Smart City؛ QNN؛ Route Planning؛ ADAS

امنیت سایبر