Neutrosophic Super-Hypergraph Fusion for Proactive Cyberattack Countermeasures: A Soft Computing Framework

Cyber defense often fails early because heterogeneous signals noisy logs, shifting baselines, and adversarial deception are collapsed into a single score. We propose a soft computing framework that keeps uncertainty explicit by representing each event with a neutrosophic triplet: truth (T), indeterminacy (I), and falsity (F). These triplets are propagated through a multi-level system model using super-hypergraphs, which naturally capture relations across processes, hosts, and subnets without flattening context. We define two simple, auditable fusion operators: a conjunctive AND at the pattern level (min for T, max for I and F) and a disjunctive OR at the entity level (max for T, min for I and F). We prove key properties (boundedness, monotonicity, commutativity, associativity, idempotence) and couple the fusion with a decision rule that triggers proactive actions when the T–F margin is sufficient and I is tolerably low. A worked case study on command-and-control and staged exfiltration demonstrates end-to-end calculations, ablation over thresholds (α, β) and unknown-context handling (γ), and comparison with crisp and fuzzy baselines. Results show earlier, steadier interventions with transparent rationale: the method prevents overreaction to isolated strong cues while still surfacing consistent evidence promptly. The framework is lightweight, interpretable, and ready for integration into security operations that require traceable, risk-aware automation.