Multi-Agent Collaborative Intrusion Detection

• The framework integrates autonomous agents that collaboratively sense, reason, and act to achieve efficient and adaptive intrusion detection.
• It employs hierarchical, federated, and fully distributed paradigms with specialized roles for sensing, optimization, and decision-making.
• Robust communication protocols and consensus algorithms ensure scalability, fault tolerance, and privacy preservation across diverse network environments.

A multi-agent collaborative intrusion detection framework is an architectural approach to detection and defense against cyber-attacks, leveraging a constellation of autonomous intelligent agents distributed within a network or system. These agents cooperate in sensing, reasoning, and decision-making, sharing local observations and inferences to support distributed, adaptive, and robust intrusion detection under operational constraints typical of modern IoT, cyber-physical, and cloud-edge environments. Technical realizations range from tightly integrated agentic loops for UAV-enabled IoT to distributed blockchain overlays and LLM-orchestrated expert ensembles. Below is a comprehensive overview of key conceptual, architectural, and methodological features across leading research on the topic.

1. Architectural Paradigms for Multi-Agent Collaborative Intrusion Detection

Three principal paradigms are dominant in the multi-agent collaborative intrusion detection literature:

• Perception–Reasoning–Action Closed-Loop Architectures: As formalized in LLM-enhanced Low-Altitude Economy IoT (LAE-IoT), three core agents (Perception & Memory, Reasoning, Adaptive Classification) form a tightly-coupled loop. Sensory data are encoded, feature-selected via LLM-guided optimization, and classified adaptively depending on dynamic resource constraints. Local agents exchange summaries or feature representations peer-to-peer or via supervisory nodes, supporting real-time adaptation and fallback under loss of connectivity (Li et al., 25 Jan 2026).
• Hierarchical and Federated Multi-Tier Architectures: HBFL’s edge–fog–cloud topology typifies federated learning for IoT security, where local edge agents train models on-device, organizational “combiners” perform local aggregation, and a global reducer on permissioned blockchain ensures verifiable orchestration and global knowledge sharing. Agents at different layers manage local detection, aggregation, validation, and inter-organizational threat intelligence dissemination (Sarhan et al., 2022).
• Cooperating Agent Hierarchies and Peer-to-Peer overlays: Fully distributed hierarchies (e.g., the Basic Agent/Workstation/Domain/Enterprise Coordinator stack) deliver total decentralization, avoiding central points of failure. Interest-directed communication supports selective data-sharing—an agent requests task-relevant data only when required, optimizing both efficiency and resilience (Sen, 2011).

The table below contrasts typical agent roles in selected paradigms:

Reference	Agent Types (Examples)	Collaboration Mechanism
(Li et al., 25 Jan 2026)	Perception, Reasoning, Adaptive Classifier	Onboard bus, gRPC star-topology
(Sarhan et al., 2022)	Edge, Organization Combiner, Reducer	Federated/blockchain, permissioned smart-contracts
(Sen, 2011)	Basic Agent, WCA, DCA, ECA, SMA	Hierarchical interest-directed messaging

2. Agent Roles, Communication, and Coordination Strategies

Role Specialization: Modern frameworks emphasize specialization of agent function:

• Sensing/Feature Learning: Perception agents integrate multi-level telemetry, converting sessions to traffic images, and extracting universal representations, often via self-supervised or diffusion-based encoders (Li et al., 25 Jan 2026).
• Reasoning/Optimization: LLM-empowered reasoning agents control feature selection and aggregation, dynamically adapting protocols such as particle swarm optimization (PSO) for feature mask selection, and mediating knowledge transfer via prompt-driven inference (Li et al., 25 Jan 2026).
• Classification/Decision: Pools of lightweight and complex classifiers enable real-time, resource-aware selection. Simple policies (e.g., under battery/CPU constraints, use lightweight model; else, use high-accuracy model) are invoked by the classification agent. Action agents trigger defensive actions upon detection (Li et al., 25 Jan 2026).
• Aggregation & Correlation: Hierarchical or overlay agents fuse and validate observations (e.g., combiners, NIDSBoardAgents), supporting multi-domain, multi-organization, or multi-tier aggregation and cross-layer attack correlation (Sarhan et al., 2022, Aguayo-Canela et al., 2024).

Collaboration Protocols: Communication leverages:

• Lightweight and structured message passing (e.g., gRPC, FIPA-ACL, JADE Agent Management System) for both synchronous and asynchronous information sharing (Li et al., 25 Jan 2026, Sen, 2011, Aguayo-Canela et al., 2024).
• Interest-driven queries, in which agents express “interests” (data/resource needs) rather than constant broadcast, optimizing bandwidth and focus (Sen, 2011).
• Blockchain-based authenticated transactions—with cryptographic signatures and smart contract enforcement—for privacy-preserving, tamper-proof inter-organizational collaboration (Sarhan et al., 2022).

Fallback and Resilience: Architectures are designed such that connectivity loss reverts local agents to autonomous operation, sacrificing only global correlation until reconnection (Li et al., 25 Jan 2026).

3. Core Detection Methodologies: Feature, Model, and Algorithmic Choices

Data Representation: Modern multi-agent detection frameworks employ advanced feature representations:

• Session-to-image encoding followed by self-supervised embedding via Denoising Diffusion Probabilistic Models (DDPMs), yielding universal features robust to topology and device heterogeneity (Li et al., 25 Jan 2026).
• Direct feature extraction from NetFlow, packet, or application logs—processed locally and aggregated via strict privacy policies (Sarhan et al., 2022, Khoa et al., 2022).
• Rule-based engines embedded in agents for rapid, distributed signature or pattern matching (e.g., Snort-based collaborative architectures, custom embedded rule engines in IoT) (Davies et al., 23 Apr 2025, Aguayo-Canela et al., 2024).

Model Selection and Adaptivity: Choice among multiple classifier models is driven by:

• Real-time resource monitoring (CPU load $u$, battery $b$), allowing dynamic trade-off between accuracy and efficiency (e.g., LightGBM vs. small CNNs) (Li et al., 25 Jan 2026).
• Offline and online distinction—heavyweight feature learning and meta-model optimization are performed solely in the cloud/edge, whereas only distilled or compact models are deployed to resource-constrained agents (Li et al., 25 Jan 2026).

Federated and Blockchain-Integrated Learning: Many frameworks train models using private local data, sharing only updates aggregated in privacy-preserving ways (e.g., FedAvg within a permissioned blockchain framework), often including explicit anomaly detection in aggregation to resist poisoning (Sarhan et al., 2022, Khoa et al., 2022).

Consensus and Conflict Resolution: In defense against both adversarial and unintentional inconsistency, some frameworks (notably CP-Guard) rely on advanced group-testing and consensus protocols (PASAC), collaborative consistency loss (CCLoss), and sliding-window adaptive thresholds to systematically detect and exclude corrupted or malicious inputs without requiring advance knowledge of attacker distribution (Hu et al., 28 Jun 2025).

4. Performance Metrics, Datasets, and Experimental Outcomes

Metrics: The field assesses frameworks via a range of explicit, formalized metrics:

• Detection Accuracy: $\frac{TP + TN}{TP + TN + FP + FN}$
• Precision: $\frac{TP}{TP + FP}$
• Recall: $\frac{TP}{TP + FN}$
• F1-score: $$2\cdot \frac{\mathrm{Precision} \times \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}$$
• Detection Latency: $$T_\mathrm{latency} = T_\mathrm{labeling} + T_\mathrm{training}$$ (usually $T_{labeling} \gg T_{training}$) (Li et al., 25 Jan 2026).

Experimental Results across Notable Frameworks:

System	Accuracy	F1-score	Latency	Resource Use	Notes
LLM-Enhanced LAE-IoT (Li et al., 25 Jan 2026)	>90%	0.91	<50 ms	<20% CPU, 60 MB	10% labeled data sufficient
HBFL (Hier. Blockchain FL) (Sarhan et al., 2022)	99.7%	≈0.94–0.99	--	--	FAR ≈ 0.1%, privacy preserved
Distributed IDS, agents (Sen, 2011)	≈91%	--	--	0.35% CPU avg	No central analysis, overall FP ≈ 13%
Collaborative Snort CIDS (Davies et al., 23 Apr 2025)	90%+	--	<2.5 s	--	TPR improved by 2–3 points over baseline

Significance: Across benchmarks (Edge-IIoTset, USTC-TFC, ISCX-VPN, NF-BoT-IoT-v2), agentic frameworks consistently outperform or reach parity with centralized or non-collaborative baselines, providing additional efficiency or privacy benefits.

5. Scalability, Adaptability, and Engineering Trade-Offs

Scalability: Distributed and hierarchical frameworks ensure that as $N$, the number of hosts or agents, increases:

• Communication and state management growth is typically $O(\log N)$ in hierarchical interest architectures (Sen, 2011).
• Agent memory footprint and local computation are strictly capped, with only summaries or compact model updates traversing the network (Li et al., 25 Jan 2026, Sarhan et al., 2022).
• Both agent mobility (in legacy mobile agent frameworks) and stateless messaging (JADE, FIPA-ACL) provide hot-swappable, modular extension, supporting new behaviors or features at runtime without service interruption (Jaisankar et al., 2010, Aguayo-Canela et al., 2024).

Fault Tolerance and Compromise Resistance: The absence of a central analysis point, combined with cryptographic measures and local thresholding, sharply limits the scope and blast radius of both system faults and active attacks (Sen, 2011, Sarhan et al., 2022).

Engineering Practices:

• For real-time and resource-limited deployments, avoid embedding heavyweight models on UAVs or edge nodes; all such computation should be offloaded to edge/gateway/cloud or distilled to compact inference agents (Li et al., 25 Jan 2026).
• LLM prompt engineering and mask caching balance network load and model freshness; queries should be limited to device-profile changes or regular intervals (Li et al., 25 Jan 2026).
• Real-time consensus/aggregation should use majority or thresholding and anomaly detection to avoid both alert fatigue and false positives, leveraging multi-agent corroboration (Davies et al., 23 Apr 2025, Aguayo-Canela et al., 2024).

6. Security, Privacy, and Adversarial Resilience

Privacy preservation: By restricting cross-agent communication to model updates or compact summaries (no raw logs), privacy leakage is minimized, with further protection via encryption in blockchain-backed or permissioned frameworks (Sarhan et al., 2022).

Adversarial Robustness: Agentic systems are especially attractive for defending against coordinated and distributed attacks:

• Collaborative group-testing, as in CP-Guard, assures consensus on benign input in the presence of arbitrary numbers ($m<n$) of malicious agents, with upper bounds on misclassification probability derived from threshold parameters $(\alpha,\beta)$ (Hu et al., 28 Jun 2025).
• Global aggregation logic, anomaly-detection at aggregation points, and smart-contract managed admission and validation combine to contain poisoned model or data updates (Sarhan et al., 2022).

A plausible implication is that multi-agent collaboration provides not only functional scalability but also redundancy and diversity, raising the bar for attackers attempting cross-organizational or distributed compromises.

7. Outlook, Lessons Learned, and Best Practices

• Universal, self-supervised representation learning decouples detection logic from topological and device changes, improving few-shot and zero-day attack detection (Li et al., 25 Jan 2026).
• Adaptive model selection based on real-time resource availability ensures operational effectiveness in highly resource-constrained environments.
• Offline/online split in LLM and meta-optimization tasks is critical for energy efficiency, suggesting a clear separation between heavy, cloud-side agents and lightweight, on-device inference nodes.
• Controlled inter-agent query frequency is crucial to maintain efficiency; re-querying LLMs only as needed avoids unnecessary overhead and stale state.
• Prompt engineering and model distillation are essential to reduce hallucination and maintain explainability in LLM-enhanced pipelines.
• Hierarchical or federated overlays reinforced by blockchain or smart-contract logic enable practical, privacy-aligned, and scalable cross-organizational collaborative security, albeit at the expense of some additional complexity and communication overhead.
• Extensibility is best supported by modular, semi-autonomous agent frameworks with hot-pluggable rule or behavior deployment (Aguayo-Canela et al., 2024).

The dominant trend is toward agentic architectures tightly integrating self-supervised, federated, and reasoning components to meet the unique challenges of modern, distributed, and adversarial cyber-physical and IoT networks (Li et al., 25 Jan 2026, Sarhan et al., 2022, Sen, 2011).