AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles
Rathin Chandra Shit, Sharmila Subudhi
TL;DR
This work tackles security and reliability challenges in autonomous vehicles by proposing an integrated framework that combines LSTM-based real-time anomaly detection on multi-sensor data with a consortium blockchain for data integrity and auditability. Automated responses are enacted through smart contracts, leveraging PBFT-style consensus for efficiency in vehicular networks. The approach is validated in CARLA with 50 autonomous vehicles and a private Hyperledger Fabric deployment, showing high precision and recall for GPS spoofing, sub-second to a few-second response times, and robust performance under Byzantine conditions. The study demonstrates enhanced safety, trust, and forensic capabilities, while noting scalability and privacy as key directions for future development.
Abstract
Autonomous Vehicles (AV) proliferation brings important and pressing security and reliability issues that must be dealt with to guarantee public safety and help their widespread adoption. The contribution of the proposed research is towards achieving more secure, reliable, and trustworthy autonomous transportation system by providing more capabilities for anomaly detection, data provenance, and real-time response in safety critical AV deployments. In this research, we develop a new framework that combines the power of Artificial Intelligence (AI) for real-time anomaly detection with blockchain technology to detect and prevent any malicious activity including sensor failures in AVs. Through Long Short-Term Memory (LSTM) networks, our approach continually monitors associated multi-sensor data streams to detect anomalous patterns that may represent cyberattacks as well as hardware malfunctions. Further, this framework employs a decentralized platform for securely storing sensor data and anomaly alerts in a blockchain ledger for data incorruptibility and authenticity, while offering transparent forensic features. Moreover, immediate automated response mechanisms are deployed using smart contracts when anomalies are found. This makes the AV system more resilient to attacks from both cyberspace and hardware component failure. Besides, we identify potential challenges of scalability in handling high frequency sensor data, computational constraint in resource constrained environment, and of distributed data storage in terms of privacy.