arXiv:2511.03993v1 Announce Type: new 
Abstract: Network anomaly detection systems encounter several challenges with traditional detectors trained offline. They become susceptible to concept drift and new threats such as zero-day or polymorphic attacks. To address this limitation, we propose a Ca$^{2+}$-modulated learning framework that draws inspiration from astrocytic Ca$^{2+}$ signaling in the brain, where rapid, context-sensitive adaptation enables robust information processing. Our approach couples a multicellular astrocyte dynamics simulator with a deep neural network (DNN). The simulator models astrocytic Ca$^{2+}$ dynamics through three key mechanisms: IP$_3$-mediated Ca$^{2+}$ release, SERCA pump uptake, and conductance-aware diffusion through gap junctions between cells. Evaluation of our proposed network on CTU-13 (Neris) network traffic data demonstrates the effectiveness of our biologically plausible approach. The Ca$^{2+}$-gated model outperforms a matched baseline DNN, achieving up to $\sim$98\% accuracy with reduced false positives and negatives across multiple train/test splits. Importantly, this improved performance comes with negligible runtime overhead once Ca$^{2+}$ trajectories are precomputed. While demonstrated here for cybersecurity applications, this Ca$^{2+}$-modulated learning framework offers a generic solution for streaming detection tasks that require rapid, biologically grounded adaptation to evolving data patterns.

تقدم دراسة جديدة إطار تعلم مُعدل بالكالسيوم مستوحى من الإشارات الأستروسيتيّة في الدماغ لتعزيز أنظمة كشف الشذوذ في الشبكات. غالبًا ما تكافح الطرق التقليدية مع انحراف المفاهيم والتهديدات الناشئة، لكن هذا النهج المبتكر يعد بتكيف سريع ومعالجة معلومات محسّنة، مما يمثل تقدمًا كبيرًا في مجال الأمن السيبراني.

Un nuevo estudio presenta un marco de aprendizaje modulado por calcio, inspirado en la señalización astrocitaria en el cerebro, para mejorar los sistemas de detección de anomalías en redes. Los métodos tradicionales a menudo luchan con el cambio de concepto y las amenazas emergentes, pero este enfoque innovador promete una adaptación rápida y un procesamiento de información mejorado, lo que representa un avance significativo en ciberseguridad.

Une nouvelle étude présente un cadre d'apprentissage modulé par le calcium, inspiré par le signalement astrocytaire dans le cerveau, pour améliorer les systèmes de détection d'anomalies réseau. Les méthodes traditionnelles ont souvent du mal avec le dérive conceptuelle et les menaces émergentes, mais cette approche innovante promet une adaptation rapide et un meilleur traitement de l'information, représentant ainsi une avancée significative en cybersécurité.

A new study introduces a calcium-modulated learning framework inspired by astrocytic signaling in the brain to enhance network anomaly detection systems. Traditional methods often struggle with concept drift and emerging threats, but this innovative approach promises rapid adaptation and improved information processing, making it a significant advancement in cybersecurity.

Multiscale Astrocyte Network Calcium Dynamics for Biologically Plausible Intelligence in Anomaly Detection

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Multiscale Astrocyte Network Calcium Dynamics for Biologically Plausible Intelligence in Anomaly Detection

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