Optimizing IoT intrusion detection with cosine similarity based dataset balancing and hybrid deep learning

Optimizing IoT intrusion detection with cosine similarity based dataset balancing and hybrid deep learning

Abstract With IoT networks expected to exceed 29 billion connected devices by 2030, the risk of cyberattacks has never been higher. As more devices come online, the attack surface for hackers continues to expand, making cybersecurity a pressing concern. Intrusion Detection Systems (IDS) are essentia...

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Bibliographic Details
Main Authors: Arvind Prasad, Wael Mohammad Alenazy, Naved Ahmad, Gauhar Ali, Hanaa A. Abdallah, Sadique Ahmad
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Class imbalance
IDS
IoT security
Deep learning
Cybersecurity
Online Access:https://doi.org/10.1038/s41598-025-15631-3
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https://doi.org/10.1038/s41598-025-15631-3

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