Year: 2025
Type: conference-paper
Abstract: Securing dynamic and heterogeneous 5G/6G network slices presents significant challenges due to evolving traffic patterns, diverse service requirements, and an increasing number of cyber threats. To address these issues, this paper introduces SAFESlice, an AI-driven adaptive framework built on Software-Defined Networking (SDN). SAFESlice provides real-time flowbased security enforcement customized to softwarized networks. Our framework integrates a trained threat detection model based on XGBoost into a customizable Ryu SDN controller, enabling live traffic classification and rapid mitigation through programmable flow rules. Experiments on a Mininet-based SDN testbed using the UNSW-NB15 data set demonstrate high detection accuracy in nine types of attacks. Although the architecture supports logical flow classification by service type, the current implementation applies uniform policies across flows; slices are logically abstracted as service flows (e.g., HTTP, VoIP), with full 3GPP-aligned slice-aware enforcement left for future work.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416735799
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