Securing Transit Data: Integrity Protection for Online Transportation System

Abstract

The increasing reliance on cloud-based technologies in transportation systems has raised significant cybersecurity concerns. As digital infrastructure expands, protecting transit data from cyber threats becomes critical. This project presents an AI-driven framework that uses honeypots to gather real attack data and machine learning models to detect and classify threats in instantly and continuously. The system enables scalable, cloud-based monitoring and automated alerts for rapid response. Testing shows over 92% accuracy in threat detection, validating the framework\'s effectiveness in enhancing transportation data security.

Introduction

The integration of cloud computing and artificial intelligence (AI) has greatly enhanced the efficiency and adaptability of modern transportation systems, enabling real-time data exchange for traffic management, incident response, and predictive maintenance. However, this increased digital connectivity also introduces significant cybersecurity risks that threaten the integrity and safety of these systems.

Transportation platforms handle vast, diverse data streams (GPS, sensors, video, user logs) centralized in the cloud for processing and analysis, which improves scalability but also expands the attack surface.

Literature review highlights various AI-driven and cloud-based security approaches for cyber-physical systems, including adaptive intrusion detection, reinforcement learning for mobile defense, lightweight cryptographic protocols, and trust-based security models. Research stresses the need for dynamic, real-time, and scalable cybersecurity solutions tailored to transportation’s unique challenges.

Proposed methodology presents a multi-layered AI-based cybersecurity architecture designed for cloud-based transportation systems. Key components include:

• Data acquisition from simulated transit sources (GPS, sensors) for training and real-time monitoring.

• Cloud-based aggregation and preprocessing that filters anomalous data and routes it to a honeypot module.

• A honeypot module that attracts and logs cyberattacks, creating labeled datasets to continuously improve AI models.

• AI detection using machine learning (Decision Trees, SVM) and deep learning (CNNs for spatial, RNNs for time-series anomalies) to identify and classify threats.

• Alert and dashboard system providing real-time notifications and threat prioritization.

• Administrative controls for threat mitigation, system tuning, and audit management.

• Scalability features enabling future integration with blockchain auditing, federated learning, and edge AI for low-latency detection.

Evaluation and results showed the system’s effectiveness through unit and integration testing, honeypot attack capture, and AI detection performance:

• Over 92% accuracy on known threats and 85% on new patterns, with high recall and low false positives.

• Real-time alerting within milliseconds and prioritized threat response.

• Robust scalability under high traffic conditions, maintaining low latency and consistent throughput.

Overall, the framework delivers a scalable, adaptive, and reliable AI-driven cybersecurity solution for protecting cloud-based smart transportation infrastructures.

Conclusion

This project presents a comprehensive, AI driven cybersecurity framework designed to safeguard cloud-based transportation systems from evolving cyber threats. In response to the increasing vulnerability of modern transit infrastructures, the proposed methodology combines layered security components, including honeypot-based threat capture, real-time anomaly detection, AI based classification, and automated alerting into a unified system. Each module contributes to an efficient workflow initiated by telemetry acquisition and culminates in intelligent threat mitigation and administrative oversight. The results achieved validate the framework’s ability to meet the core objectives outlined in the problem statement. The system demonstrated elevated recognition precision for both known and novel attack patterns, rapid alert generation with low latency, and reliable data handling under high-load conditions.

References

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Copyright

Copyright © 2025 Sandhya Rani Maddani, Mrs. Jennifer Mary S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.