@article{Moghaddam2026, 
author = {Pardis Sadatian Moghaddam and Mahyar Mahmoudi and Nuria Serrano and Francisco Hernando-Gallego and Diego Martín and José Vicente Álvarez-Bravo},
title = {A Novel Hybrid Evolutionary Transformer-Long Short-Term Memory Model for Unified Anomaly Detection in IoT and Cyber-Physical Networks},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {91},
keywords = {Internet of Things, cyber-physical systems, intrusion detection datasets, transformer, long short-term memory, chimp optimization algorithm},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.081311},
doi = {10.32604/cmc.2026.081311},
abstract = {The rapid proliferation of the Internet of Things (IoT) and cyber-physical systems (CPS) within critical infrastructure sectors has significantly expanded the attack surface for advanced and stealthy cyber threats. Since these systems increasingly rely on real-time data exchange and autonomous control, developing intelligent, scalable, and adaptive anomaly detection mechanisms has become a pressing requirement. This paper proposes a novel hybrid framework, evolutionary-transformer-long short-term memory (Evo-Transformer-LSTM), that integrates the temporal modeling capability of LSTM networks, the global attention mechanism of Transformer encoders, and the optimization power of the improved chimp optimization algorithm (IChOA) for hyper-parameter tuning. In the proposed architecture, the Transformer encoder extracts high-level contextual patterns from traffic sequences, while the LSTM component captures local temporal dependencies. The framework is rigorously evaluated on four benchmark datasets from the Canadian Institute for Cybersecurity (CIC): CIC-IDS-2017, CSE-CIC-IDS-2018, CIC IoT-DIAD (2024), and CICIoV (2024). Comparative experiments are conducted against several state-of-the-art baselines, including transformer, LSTM, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), convolutional neural network (CNN), k-nearest neighbors (KNN), and random forest (RF) classifiers. Results show that the proposed Evo-Transformer-LSTM achieves up to 98.25% accuracy, an F1-score of 97.91%, and an area under the curve (AUC) of 99.36% on CIC-IDS 2017, while maintaining above 96% accuracy and 98% AUC even on the more challenging CICIoV 2024 dataset, consistently surpassing all baseline models. In addition, statistical significance tests confirm the superiority of the proposed approach. In conclusion, Evo-Transformer-LSTM offers a unified, scalable, and robust solution for anomaly detection in modern IoT and CPS infrastructures, with potential for real-world deployment in security-sensitive domains.}
}