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

Abstract

Cyber defense often fails early because heterogeneous signals noisy logs, shifting 
baselines, and adversarial deception are collapsed into a single score. We propose a softcomputing 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. 

DOI: 10.5281/zenodo.17042070