Federated Threat Intelligence Sharing

• Federated threat intelligence sharing is a collaborative paradigm where multiple entities securely share cyber threat data without exposing raw sensitive information.
• Key methodologies include federated learning, blockchain-based exchanges, and multiparty encryption to ensure privacy, data quality, and regulatory compliance.
• This approach enhances threat detection accuracy and operational resilience by reducing centralized risks and mitigating free-riding in data contributions.

Federated threat intelligence sharing is a collaborative paradigm in which multiple autonomous entities—such as organizations, enterprises, or infrastructure operators—jointly collect, exchange, and analyze indicators of compromise, attack patterns, and vulnerability information without centralizing sensitive underlying data. Leveraging technical architectures such as federated learning, multiparty computation, blockchain-based exchanges, and privacy-preserving representation techniques, these systems enable effective cyber defense and situational awareness while providing rigorous guarantees of privacy, security, and regulatory compliance across organizational or jurisdictional boundaries.

1. Motivation and Core Principles

The impetus for federated threat intelligence sharing arises from both technical and organizational requirements. While centralized models yield complete visibility, they also entail serious risks: loss of data control, exposure of proprietary or sensitive information, increased attack surface, and legal barriers imposed by privacy or compliance frameworks. Federated architectures reconcile these concerns by ensuring that substantive threat intelligence (e.g., indicators, behavioral patterns, detection models) is shared, but the actual sensitive data remains local.

Foundational principles include:

• Privacy preservation: No raw datasets (such as network logs, user behavior, or prompt contents) ever leave the originating organization; only processed representations or model updates are exchanged, minimizing risk of data leakage (Sarhan et al., 2021, Wang et al., 25 Feb 2025, Gill et al., 6 Sep 2025).
• Decentralization: There is no single point of failure; aggregation (typically of model parameters, knowledge summaries, or anonymized fingerprints) may be orchestrated via servers, blockchains, or peer-to-peer protocols (Demertzis, 2021, Arikkat et al., 20 Jun 2024).
• Data and contribution quality: Contemporary systems incorporate metrics beyond traditional volume-based approaches, evaluating the correctness, relevance, utility, and uniqueness of shared indicators to detect and discourage free-riding and maximize actionable benefit (Al-Ibrahim et al., 2017).
• Interoperability: Adoption of standardized formats (e.g., STIX, AITI) and protocols (e.g., TAXII, CoAP/OSCORE) enables seamless integration with legacy cyber defense and threat exchange frameworks (Nguyen et al., 2022, Iacovazzi et al., 19 Jun 2024, Papanikolaou et al., 2023).

2. Technical Architectures and Methodologies

Model- and Data-Centric Approaches

• Federated Learning (FL): Each participant trains a local machine learning or deep learning model on private data. Model parameters (weights, gradients) are periodically aggregated to update a global model without direct data exchange. Classical aggregation follows $w_{t+1} = \sum_k \frac{n_k}{n} w_{k,t}$, supporting both IID and non-IID data distributions (Sarhan et al., 2021, Thi et al., 2023, Wang et al., 25 Feb 2025, Chennoufi et al., 7 Jul 2025).
• Swarm Learning and Blockchain: Decentralized training is augmented with distributed ledgers for tamper-proof auditability, access control, and reputation management. Smart contracts orchestrate model update validation, quality assessment, and contributor on-boarding, removing any single point of trust (Arikkat et al., 20 Jun 2024, Demertzis, 2021, Allouche et al., 2021).
• Multiparty Homomorphic Encryption: Joint statistics or model gradients are computed over encrypted data using shared public keys, so intermediate and final results remain confidential throughout the computation lifecycle (Trocoso-Pastoriza et al., 2022).
• Privacy-Preserving Fingerprinting: Suspicious content (e.g., LLM prompts) is transformed into privacy-preserving fingerprints by sequentially applying PII redaction, semantic embedding, binary quantization, and randomized response mechanisms. This produces non-invertible representations suitable for cross-organization threat matching, with tunable privacy-utility trade-offs (Gill et al., 6 Sep 2025).

Evaluation and Enhancement

• Active and Adaptive Learning: Advanced systems incorporate active learning to continuously discover new threats in unlabeled data, and adaptive aggregation strategies (e.g., simulated annealing, attention mechanisms) to optimize learning and convergence under heterogeneity and resource constraints (Ongun et al., 2022, Neto et al., 2022, Belenguer et al., 2022, Li et al., 2023).
• Prototype and Analytic Knowledge Sharing: Some frameworks exchange high-level knowledge such as class prototypes—average feature representations for attack classes—supporting few-shot and zero-shot detection of rare, unseen, or adversarial threats (Chennoufi et al., 7 Jul 2025).

3. Data Quality, Contribution Evaluation, and Free-Riding Mitigation

Traditional metrics for participation in information sharing have emphasized volume—number of indicators, samples, or model updates submitted. This approach is susceptible to manipulation (free-riding), where entities maximize reward with minimal substantive contribution.

Recent work formalizes a multi-dimensional Quality of Indicators (QoI) metric, where:

• Correctness: Degree of label or attribute match to a reference or ground-truth evaluation, e.g., via Venn comparison to classifier predictions.
• Relevance and Utility: Community-specific priorities (e.g., weighting based on attack class importance) and feature-level informativeness (derived via information gain or PCA).
• Uniqueness: Non-redundancy, assessed using similarity metrics such as Mahalanobis distance.

Score aggregation is performed as:

$$\text{QoI}(X_i) = \omega_C \cdot C(X_i) + \omega_R \cdot R(X_i) + \omega_U \cdot U(X_i) + \omega_N \cdot N(X_i)$$

where $\omega_*$ are normalized weights for each component (Al-Ibrahim et al., 2017).

This paradigm is empirically demonstrated to better identify meaningful and actionable contributions, reduce the risk of polluted or low-value data dissemination, and enhance overall system efficacy.

4. Privacy, Security, and Trust Mechanisms

The extension of federated approaches to sensitive or highly regulated domains mandates robust privacy and security assurances:

• Confidentiality is enforced by local data residency, cryptographic protections (homomorphic encryption, blockchain smart contracts), and, where applicable, PI-preserving fingerprinting (Trocoso-Pastoriza et al., 2022, Gill et al., 6 Sep 2025).
• Integrity and accountability are supported through tamper-resistant ledgers, digital signature schemes, and separation of identity and activity within blockchain overlays (Allouche et al., 2021).
• Verifiability and trustworthiness are provided via zero-knowledge proofs and reputation scoring. For example, SeCTIS uses validator nodes to submit proofs of correct model evaluation without leaking test set details; participant reputations are updated as exponential moving averages of trust scores, which are derived from model output agreement with consensus (Arikkat et al., 20 Jun 2024).
• Differential privacy and cryptographic noise injection are employed in several systems, both when aggregating model updates and when sharing representations, to reduce risks of input reconstruction or unintended information leakage (Wang et al., 25 Feb 2025, Gill et al., 6 Sep 2025, Chennoufi et al., 7 Jul 2025).

5. Interoperability, Standardization, and Lightweight Exchange

Real-world deployment of federated threat intelligence sharing requires compatibility with a vast array of legacy and emerging data formats, protocols, and device constraints:

• Standardization: Widespread use of formats such as STIX, its AI-specific extension AITI, and SIGMA rules for both encoding and sharing cyber and AI-specific threat indicators (Nguyen et al., 2022, Papanikolaou et al., 2023).
• Protocol compatibility: Integration with TAXII, OpenDXL, and MISP enables federated systems to easily replace or overlay traditional centralized sharing methods (Allouche et al., 2021, Nguyen et al., 2022, Iacovazzi et al., 19 Jun 2024).
• Lightweight encoding: For resource-constrained settings (notably IoT), systems employ tinySTIX (STIX compressed with integer encoding and CBOR), secure serializations (COSE), and constrained protocols (CoAP, OSCORE) to ensure interoperability without excessive overhead (Iacovazzi et al., 19 Jun 2024).
• Heterogeneous modality support: Multimodal LLMs in federated architectures fuse knowledge from network traffic, logs, sensor feeds, and even images to facilitate comprehensive threat analysis (Wang et al., 25 Feb 2025).

6. Performance, Benchmarks, and Deployment Considerations

Empirical studies demonstrate the technical and operational viability of federated threat intelligence sharing. Key findings include:

• Detection metrics: Systems achieve up to 96.4% detection accuracy, improved macro-averaged F1 by up to 23% in non-IID scenarios, and can reduce false positives by up to 7% over centralized baselines (Wang et al., 25 Feb 2025, Chennoufi et al., 7 Jul 2025, Li et al., 2023).
• Efficiency and scalability: Techniques such as metaheuristic-optimized hyperparameters, split learning (e.g., Fed-urlBERT), and distributed storage (e.g., IPFS) yield substantial gains in both message passing, storage footprint, and response latency (e.g., training in 180s and detection in 3.8s for a 10TB dataset) (Li et al., 2023, Wang et al., 25 Feb 2025, Arikkat et al., 20 Jun 2024).
• Practical resilience: Active learning modules, model poisoning defenses (e.g., DTrust), validator-based exclusion, and prototype regularization manifest robust performance in adversarial and heterogeneous environments (Ongun et al., 2022, Chennoufi et al., 7 Jul 2025, Arikkat et al., 20 Jun 2024).

7. Challenges, Limitations, and Future Directions

Despite demonstrated promise, several challenges remain:

• Data heterogeneity: Real-world attack classes are unevenly distributed (non-IID), requiring advanced alignment and augmentation (e.g., adversarial training, prototype sharing) to avoid overfitting and underrepresentation (Chennoufi et al., 7 Jul 2025, Gayathri et al., 19 Sep 2024).
• Quality assurance: Reputation and trust mechanisms must be robust against targeted attacks (e.g., Byzantine participants, collusion, label flipping); ongoing developments focus on integrating zero-knowledge proofs and adaptive validation (Arikkat et al., 20 Jun 2024).
• Resource optimization: Communication and computation overhead, particularly in multimodal and LLM-based frameworks, require further algorithmic refinement, including asynchronous updates, attention-based participant selection, and enhanced hardware acceleration (Belenguer et al., 2022, Wang et al., 25 Feb 2025).
• Policy and compliance: Systems such as BinaryShield exemplify the operationalization of privacy-by-design, enabling regulatory compliance while sustaining effective cross-organization intelligence sharing—a critical concern as regulations worldwide become more stringent (Gill et al., 6 Sep 2025, Allouche et al., 2021, Trocoso-Pastoriza et al., 2022).
• Expanding domains: Recent advances propose the extension of federated sharing to support AI/ML vulnerability intelligence, support for new device types (IoT/IIoT), and modalities beyond text and logs, paving the way for next-generation, adaptive defense collaborations (Nguyen et al., 2022, Iacovazzi et al., 19 Jun 2024, Wang et al., 25 Feb 2025).

Federated threat intelligence sharing thus constitutes a rapidly evolving field balancing actionable security collaboration with the realities of privacy, scale, and regulatory boundaries, supported by diverse technical methods spanning federated learning, cryptographic privacy, and advanced knowledge representation.