Unified Anomaly Detection in IoT and Cyber-Physical Networks Using Evo-Transformer-LSTM: Validation on Four CIC Benchmarks

Pardis Sadatian Moghaddam; Mahyar Mahmoudi; Nuria Serrano; F. Hernando-Galego; Diego Martín

doi:10.20944/preprints202512.0763.v1

Publication record · 2025

Unified Anomaly Detection in IoT and Cyber-Physical Networks Using Evo-Transformer-LSTM: Validation on Four CIC Benchmarks

Authors: Pardis Sadatian Moghaddam · Mahyar Mahmoudi · Nuria Serrano · F. Hernando-Galego · Diego Martín
Year: 2025
Publisher: MDPI
Journal or publication: Preprints, 2025
Google Scholar citations: 1

Research summary

Evo-Transformer-LSTM combines global attention, local temporal dependencies and evolutionary hyperparameter tuning for unified anomaly detection in IoT and cyber-physical systems. Validation spans four CIC benchmarks covering conventional, IoT and vehicular traffic.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.20944/preprints202512.0763.v1 ↗ · Open-access version ↗ · View Google Scholar record ↗