Abstract
This publication introduces NeuraSentry™, a neuromorphic anomaly detection framework inspired by biological spike-based learning and synaptic plasticity. It combines competitive vector detectors, temporal spike traces, and retroactive learning signals to detect evolving behavioral fraud and insider threats in both SaaS and on-premise systems. Key innovations include sparse activation using cosine similarity, HDBSCAN-based meta-neuron clustering, and delayed reward modulation modeled after dopamine and serotonin systems. NeuraSentry delivers adaptive, explainable, and scalable behavioral threat detection, without relying on labeled training data or fixed rules. This work establishes prior art to prevent overly broad patents in the field of neuromorphic security analytics.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Witt, Marcel, "A Neuromorphic Spike-Based System for Real-Time Behavioral Anomaly Detection in SaaS and Self-Hosted Environments", Technical Disclosure Commons, (July 08, 2025)
https://www.tdcommons.org/dpubs_series/8315