A hybrid inference pipeline for IDS: Combining DNNs and XGBoost through stacking for real-world intrusion detection

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Project publication · Ad Hoc Networks

Summary

The article proposes a hybrid inference pipeline for intrusion detection that combines DNN models, a RandomForest meta-learner and multiclass classification with XGBoost.

Authors: Antonio Villafranca, Maria-Dolores Cano

Journal: Ad Hoc Networks

DOI: 10.1016/j.adhoc.2026.104227

Funding: PID2023-148214OB-C21.

Editorial note: The project funding is recorded in the editorial corrigendum associated with the article.

Bibliometric indicators

JCR quartileQ1
Impact factor4.8
Google Scholar citations0
JCR area
Telecommunications
JCR year
2024
Scholar query
21 May 2026

Main contribution

The contribution integrates deep learning and hierarchical ensembling to maintain high accuracy without losing operational feasibility. The design covers data preparation and training through to inference on GPUs and embedded platforms.

Link with BoND1

The link with BoND1 is in the cybersecurity line for IoT and edge: the approach helps detect malicious traffic in connected infrastructures and design adaptive defense mechanisms for non-cellular B6G networks.

DOI
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