Integrating Consortium Blockchain and Attribute-Based Searchable Encryption for Automotive Threat Intelligence Sharing Model

Integrating Consortium Blockchain and Attribute-Based Searchable Encryption for Automotive Threat Intelligence Sharing Model

연구 분야: Safety

학회: International Conference on Science of Cyber Security

Sharing cyber threat intelligence (CTI) enhances organizations’ abilities in threat detection and emergency response, fostering a proactive defense approach of “predict and prevent”. Given the sensitive nature of automotive CTI and trust issues inherent in its sharing process, this paper presents an automotive CTI security sharing model that integrates blockchain and attribute-based searchable encryption. Leveraging consortium blockchain for CTI sharing, our model addresses the single point of failure and distrust in centralized systems through blockchain’s decentralization and immutability. Furthermore, by combining attribute-based searchable encryption algorithms with smart contracts, we achieve fine-grained access control and ciphertext retrieval for automotive CTI data. This enables data users to independently search CTI on the blockchain, mitigating the risk of sensitive information disclosure. Additionally, we store encrypted CTI data off-chain in the InterPlanetary File System (IPFS) to alleviate blockchain’s storage burden. Finally, we develop an automotive CTI sharing prototype system to demonstrate the feasibility and effectiveness of our proposed model.