AI-Based Threat Intelligence Sharing on Decentralized Networks

Abstract

Cyber threat intelligence (CTI) has become indispensable for anticipating, detecting, and mitigating sophisticated attacks that evolve faster than any single organization can track. Traditional, hub-and-spoke CTI exchanges—centralized repositories, mailing lists, or vendor-managed portals—struggle with timeliness, trust, privacy, and single-point-of-failure risks. This manuscript proposes and elaborates an AI-based, privacy-preserving threat intelligence sharing framework built atop decentralized networks. Artificial intelligence components automate ingestion from heterogeneous sources, extract indicators of compromise (IOCs) and tactics, techniques, and procedures (TTPs), correlate multi-source signals across organizations, and continuously score indicator quality, confidence, and relevance. A decentralized substrate—using permissioned distributed ledgers and peer-to-peer overlays—ensures provenance, immutability, and resilient dissemination while enforcing fine-grained access policies (e.g., Traffic Light Protocol), lineage tracking, and incentive-compatible governance. We review the state of the art in CTI formats (STIX/TAXII), sharing platforms (MISP, OpenCTI), and knowledge bases (MITRE ATT&CK). We then synthesize advances in federated learning, secure aggregation, differential privacy, and private set intersection to support collaborative analytics without exposing sensitive telemetry. The methodology section details an end-to-end architecture: (1) AI pipelines for NLP-based IOC/TTP extraction, graph learning for cross-organizational correlation, anomaly detection for novel threats, and active learning loops with analysts; (2) a decentralized trust layer with verifiable credentials, reputation-weighted consensus, and smart contracts for curation and slashing; (3) privacy-preserving protocols for secure multi-party collaboration; and (4) interoperability bridges to existing CTI ecosystems via STIX 2.1/TAXII 2.1 and MISP connectors. A results section presents a proof-of-concept evaluation and design-space trade-offs (precision/recall, timeliness, deduplication, ledger latency, and byzantine robustness). The paper concludes with limitations and practical adoption pathways for sectoral ISACs/ISAOs, critical infrastructure operators, and multinational consortia.