Consensus-Based Threat Validation (CVT)

• Consensus-Based Threat Validation (CVT) is a distributed methodology that leverages weighted, multi-source scoring to authenticate and prioritize cybersecurity threats.
• It integrates statistical, behavioral, and signature-based detection across IIoT, federated learning, and vulnerability management to achieve high detection accuracy and low latency.
• The framework employs Byzantine fault tolerance and adaptive thresholding to ensure reliable threat response even in adversarial environments.

Consensus-Based Threat Validation (CVT) is a distributed methodology in cybersecurity and machine learning systems that operationalizes collective decision-making among autonomous agents or federated clients to rigorously authenticate, prioritize, and act on threat intelligence or anomalous updates. In CVT architectures, consensus is not merely majority voting but typically incorporates weighted, adaptive, and sometimes multi-stage verification, leveraging statistical, behavioral, cryptographic, and domain-specific evidence sources. The framework is prominent in industrial IoT (IIoT) network defense (Singh et al., 24 Jan 2026), federated learning security (Alsulaimawi, 2024), and vulnerability management chaining (Shimizu et al., 2 Jun 2025), each with tailored formalism and implementation.

1. System Models and Notation

In IIoT grid protection scenarios, CVT operates over a peer-to-peer swarm of $n$ AI agents embedded in edge gateways, each agent denoted $a_i$, with network size constrained by $n\geq3f+1$ to maintain Byzantine fault tolerance for up to $f$ malicious or faulty nodes (Singh et al., 24 Jan 2026). Threats are formalized as events $T$ with identifiers $T_{id}$, locally scored by agents using weighted combinations:

$$\theta_i = w_s\theta_{s,i} + w_b\theta_{b,i} + w_m\theta_{m,i}, \quad w_s + w_b + w_m = 1$$

Here, $\theta_{s,i}$ is a statistical anomaly score (EWMA), $\theta_{b,i}$ a behavioral RNN output, and $\theta_{m,i}$ a signature-matching detector. Each agent also maintains a dynamic reputation $\rho_j$ and applies a distance-weight decay $$d_{ij} = \exp(-\alpha \cdot \mathrm{dist}(a_i, a_j))$$ with tunable $\alpha$.

In federated learning environments, CVT centers on the validation of model updates $\Delta w_i$ proposed by $N$ clients. Threat (or anomaly) scores $S(\Delta w_i)$ are computed via cross-entropy on a trusted labeled dataset $D_{trusted}$ (Alsulaimawi, 2024). The adaptive threshold is controlled by:

$$\tau_t = \mu_t + \alpha \sigma_t, \quad \mu_t = \frac{1}{N}\sum_{i=1}^N S(\Delta w_i)$$

where $\sigma_t$ is the standard deviation of scores.

In vulnerability management, consensus is defined as convergence among at least two independent sources—KEV (binary exploitation evidence), EPSS (probabilistic prediction), and CVSS (impact metric)—on the prioritization of each CVE. No explicit weighting or aggregation is performed; thresholds and decision-tree branching instantiate the consensus logic (Shimizu et al., 2 Jun 2025).

2. Formal Consensus Algorithms and Workflow

In IIoT swarms (Singh et al., 24 Jan 2026), the CVT protocol proceeds in roles:

• Initiator $a_i$: Upon detecting an anomaly $\theta_i > \tau_{alert}$, $a_i$ broadcasts a VOTE_REQUEST, collects responses $v_j$ from peers, and computes the aggregate score:

$$\Theta_{agg} = \frac{\sum_{j \in \mathcal{R}} v_j}{\sum_{j \in \mathcal{R}} \rho_j \cdot d_{ij}}$$

Consensus is reached when $\Theta_{agg} > \tau_{consensus}$ and $|\mathcal{R}| \geq T_q$, with $T_q = \lceil \frac{n + f + 1}{2} \rceil$. Quarantine is triggered; otherwise, monitoring ensues.

• Peer $a_j$: On reception of VOTE_REQUEST, computes a local evaluation $\theta_j = f_{detect}(\varphi_i, D_j)$, applies $\rho_j$ and $d_{ij}$, responds with $v_j$, and reputation updated via EWMA.

In federated learning (Alsulaimawi, 2024), each round:

1. Server distributes global model to clients.
2. Clients train locally and compute $S(\Delta w_i)$.
3. The server collects scores, computes threshold $\tau_t$, tallies votes $v_i$.
4. Update is accepted if $\sum_i v_i \geq \lfloor N/2 \rfloor + 1$.
5. Aggregation is performed (e.g., FedAvg) only over accepted updates.

In vulnerability management chaining (Shimizu et al., 2 Jun 2025), consensus is operationalized in a two-stage decision tree:

1. Threat relevance: KEV = 1 OR EPSS $\geq$ 0.088.
2. If threat-relevant, escalate based on CVSS $\geq$ 7.0 (high priority) or $<$ 7.0 (medium priority).

No convex combination or score is computed. The system relies solely on agreeance and threshold-testing.

3. Consensus Rule Formalization and Byzantine Tolerance

Byzantine fault tolerance is proven for CVT variants with $n \geq 3f + 1$, supporting correct operation with up to $f$ compromised agents (Singh et al., 24 Jan 2026). Consensus rounds terminate in $O(1)$ parallel exchanges, ensuring sub-millisecond convergence. The aggregation function $\Theta_{agg}$ weights votes by reputation and distance, further mitigating sybil attacks and collusion.

In federated learning, majority rule over adaptive thresholded anomaly scores drives update acceptance. While not Byzantine-resilient in the cryptographic sense, majority consensus significantly reduces adversarial model poisoning, with detection rates $>$85% for label-flip attacks under 20% adversarial population (Alsulaimawi, 2024).

Vulnerability management consensus is implicitly robust against single-source false positives by requiring joint elevation signals. However, no formal adversarial resilience analysis is provided.

4. Computational Complexity and Communication Overhead

In IIoT DMAS CVT (Singh et al., 24 Jan 2026), message complexity is $O(n)$; each consensus round comprises one broadcast and up to $n-1$ responses. The protocol is engineered for low-latency operation, achieving mean consensus times $\bar t_c = 0.85$ ms for 25-agent swarms, sublinear to network size. Only constant-sized (<256 B) UDP multicast messages are exchanged, ensuring minimal bandwidth impact (320 MB/s compared to 2 850 MB/s for centralized IDS).

Federated learning CVT (Alsulaimawi, 2024) incurs $O(N \cdot |D_{trusted}|)$ computational overhead per round due to score computation and $O(N)$ aggregation workload for the server. Communication is increased by one scalar per client per round. Privacy leakage is minimized to exposed score identities and may be mitigated with MPC or added noise.

In vulnerability management chaining (Shimizu et al., 2 Jun 2025), consensus is achieved via daily batch evaluation, with negligible per-item computational cost; scaling is driven by the size of CVE corpus and feed synchronizations.

5. Empirical Effectiveness and Experimental Results

IIoT DMAS CVT (Singh et al., 24 Jan 2026) achieves:

• 97.3% true-positive malicious detection at scale (2,000 devices), 3.8% false positive, and 87% zero-day detection.
• Sub-millisecond latency (mean 0.85 ms), scalable to 10,000 devices without throughput loss.
• 89% reduction in bandwidth versus centralized cloud IDS.
• Even with 30% Byzantine agents, >95% detection accuracy is retained.

Federated learning CVT (Alsulaimawi, 2024) demonstrates:

• Attack detection rates of 90.26% (MNIST) and 85.33% (CIFAR-10) under 20% label-flipping adversaries.
• Final test accuracy exceeds 92% (CIFAR-10) and 99.47% (MNIST).
• False positive rates of 0% (MNIST) and 2% (CIFAR-10); false negatives never exceed 10%.

Vulnerability management chaining (Shimizu et al., 2 Jun 2025) empirically yields:

• Efficiency improvement of 14–18× versus baseline CVSS policies.
• Maintained coverage of exploited vulnerabilities at >85%, with prioritization workload cut by ≈95% (to ~850 urgent issues versus ~16,000 CVSS baseline).
• Unique coverage: 57 additional exploited CVEs identified only through joint KEV-EPSS logic.

6. Practical Implementation and Applicability

IIoT CVT deployments require edge AI agents, UDP multicast protocols (<256 B message size), reputation updating (EWMA), quorum management, and integration with firewall and SIEM systems. Real-time isolation (“digital immune”) is realized at $<$1 ms convergence.

Federated learning CVT is implemented via score computation on a centrally held trusted set, adaptive thresholding, majority voting, and conditional aggregation. The approach is compatible with standard FedAvg workflows and minimal privacy exposure.

Vulnerability management chaining operates through automated, daily threshold-based querying of NVD (CVSS), FIRST (EPSS), and CISA (KEV) sources, mapped by decision tree; implementation requires only open-source feeds and scripting for ticketing or SIEM integration.

7. Limitations and Interpretations

In vulnerability management, consensus is operationalized in a decision tree rather than a weighted-sum or convex-combination formula; no formal optimization is performed. The dependence on open-source feeds is both a democratizer and a limiting factor—coverage and signal quality are bounded by KEV/EPSS model completeness. CVSS thresholding may omit technically severe but marginally scored issues.

In IIoT and FL, consensus strength is closely linked to reputation and anomaly weighting; tie-breaking and adaptive thresholding ensure conservative response but may result in deferred quarantine on ambiguous evidence. Privacy and computational overhead are explicitly accounted for; practical trade-offs are minimal for target environments.

A plausible implication is that CVT mechanisms, while highly effective under current operational models, will require further adaptation to counter emerging multi-stage adversarial strategies and to incorporate advanced cryptographic protections for highly sensitive sectors.