Toward an Intrusion Detection System for a Virtualization Framework in Edge Computing

• URL: http://arxiv.org/abs/2511.09068v1
• Date: Thu, 13 Nov 2025 01:30:19 GMT
• Title: Toward an Intrusion Detection System for a Virtualization Framework in Edge Computing
• Abstract summary: This work explores the deployment of the Lightweight Deep Anomaly Detection for Network Traffic (LDPI) integrated as an isolated service within a virtualization framework.<n>LDPI, adopting a Deep Learning approach, achieved strong training performance, reaching AUC 0.999 (5-fold mean) across the evaluated packet-window settings.<n>We deploy LDPI on a laptop-class edge node and evaluate its overhead and performance in two scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Edge computing pushes computation closer to data sources, but it also expands the attack surface on resource-constrained devices. This work explores the deployment of the Lightweight Deep Anomaly Detection for Network Traffic (LDPI) integrated as an isolated service within a virtualization framework that provides security by separation. LDPI, adopting a Deep Learning approach, achieved strong training performance, reaching AUC 0.999 (5-fold mean) across the evaluated packet-window settings (n, l), with high F1 at conservative operating points. We deploy LDPI on a laptop-class edge node and evaluate its overhead and performance in two scenarios: (i) comparing it with representative signature-based IDSes (Suricata and Snort) deployed on the same framework under identical workloads, and (ii) while detecting network flooding attacks.

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