AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles

TL;DR

This paper presents an innovative AI and blockchain-based framework for real-time anomaly detection, data integrity, and automated response in autonomous vehicles to enhance security and reliability.

Contribution

It introduces a novel integration of AI with blockchain technology for anomaly detection, data provenance, and automated responses in AVs, addressing security and trust issues.

Findings

Effective detection of cyberattacks and hardware malfunctions using LSTM networks.

Secure storage of sensor data and alerts on blockchain for data integrity.

Automated response mechanisms via smart contracts improve system resilience.

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.