Neutrosophic Cybersecurity Intelligence for Self-Healing Cellular Networks

Self-healing cellular networks must detect faults and attacks, decide under uncertainty, and act without human supervision. We introduce a compact neutrosophic decision layer that represents each time window by a triple (T, I, F): evidence of harm (T), uncertainty in the data (I), and evidence for a benign explanation (F). A simple policy, S = αT + βI − γF, compares the combined score to a fixed threshold to trigger actions. We define the features, normalization to [0, 1], constants, and time windows so every step is reproducible and auditable. The method is demonstrated in four scenarios: (S1) RF degradation/jamming-like interference, (S2) mobility and handover faults, (S3) RAN-level security anomalies, and (S4) multimodal O-RAN intrusions that combine traffic and radio signals. For each case, we compute (T, I, F) and S from realistic measurements and show the resulting actions (e.g., retuning or guided handover, neighbor-list repair, rate-shaping, scheduler re-weighting, short isolation). Across scenarios, the model stays interpretable and cautious: high T drives decisive steps, high I slows them when data are shaky, and high F prevents false alarms during known benign events. This provides a practical, transparent path to cybersecurity-aware self healing in modern cellular networks.