Multimodal Machine Learning for Cybersecurity in Internet of Things Environments: A Literature Review

Abstract: This research provides a comprehensive synthesis of Multimodal Machine Learning (MML) as a transformative paradigm for IoT defense. By integrating heterogeneous data streams, including network flow statistics, device-level telemetry, and behavioral biometrics, MML architectures facilitate a holistic understanding of system states. The algorithmic advancements that were analyzed are classified into hybrid CNN-RNN structures and state-of-the-art cross-modal Transformers, and evaluate their performance across benchmark datasets such as ToN-IoT and CICIoT2023. Quantitative results show that cross-modal Transformers achieve F1-scores between 0.95 and 0.99 across detection tasks, while hybrid CNN-LSTM models range from 0.89 to 0.96. Furthermore, this study addresses the technical "optimization triad" of pruning, quantization, and edge-cloud orchestration required to deploy these models on resource-constrained hardware.

Abdelaaziz NASSIRI, Azeddine Khyat, Kama El Guemmat and Mohamed Aazi. “Multimodal Machine Learning for Cybersecurity in Internet of Things Environments: A Literature Review”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170510

@article{NASSIRI2026,
title = {Multimodal Machine Learning for Cybersecurity in Internet of Things Environments: A Literature Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170510},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170510},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Abdelaaziz NASSIRI and Azeddine Khyat and Kama El Guemmat and Mohamed Aazi}
}