A Survey on A Hybrid CNN + Transformer with Genetic Algorithm for Intrusion Detection in Wireless IoT Devices

DOI

10.2991/978-94-6239-616-6_19How to use a DOI?

Keywords

Wireless IoT Security; Intrusion Detection System; CNN-Transformer Hybrid; Self-Attention; Genetic Algorithm; Edge Deployment; Real-time Dashboard; Resource-Constrained Devices; Cybersecurity

Abstract

Wireless IoT devices are all around us in smart homes, hospitals, factories, and fields quietly sensing and sharing data to make life easier and safer. But because they run on tiny batteries, have almost no memory, and talk over open airwaves, they are incredibly easy to attack. Traditional security tools either kill the battery or fail against new threats no one has seen before. This is why researchers have been building smarter, self-learning intrusion detection systems that can actually live on these constrained devices. Today’s best designs mix Convolutional Neural Networks (which catch suspicious patterns inside single packets) with Transformers (which understand how traffic behaves over time) and use attention to focus only on the few events that matter. To keep everything small and fast, Genetic Algorithms automatically try thousands of combinations and pick the leanest, most effective version. Even with years of progress, truly practical, battery-friendly systems for real wireless IoT networks have stayed rare. This survey traces that journey and presents our complete, ready-to-use solution: a compact CNN-Transformer with attention, optimized by genetic algorithm, and wrapped in a live Streamlit dashboard that lets anyone monitor and protect their network in real time.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. Vijayakumar
AU  - S. Yogini
AU  - R. Jerin Lucia
AU  - B. Neha
PY  - 2026
DA  - 2026/03/31
TI  - A Survey on A Hybrid CNN + Transformer with Genetic Algorithm for Intrusion Detection in Wireless IoT Devices
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 223
EP  - 237
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_19
DO  - 10.2991/978-94-6239-616-6_19
ID  - Vijayakumar2026
ER  -