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Enhancing IoT Network Security Through Deep Learning-Powered Intrusion Detection System
Subject area: Science,Engineering and Technology · Area of research: Computer Science
Abstract
The explosive growth of the Internet of Things (IoT) has introduced increased connectivity and automation with increased cybersecurity threats, especially on resource-constrained devices. Traditional intrusion detection systems (IDS) cannot respond to these dynamic threats due to their reliance on known attack patterns and lack of adaptability. This work investigates the application of deep learning (DL) techniques, in the form of a hybrid Convolutional Neural Network?Long Short-Term Memory (CNN-LSTM) model, to improve IDS performance for IoT networks. The proposed model was evaluated and tested on three publicly available benchmark datasets?CICIoT2023, Bot-IoT, and UNSW-NB15?and has been shown to have improved accuracy, precision, and recall compared to traditional machine learning models. Despite DL's typically high computational requirements, the model showed efficient performance with minimum latency, and it was a suitable candidate for the edge. The results confirm the potential of DL-based IDS to provide robust, adaptive, and elastic solutions for IoT security against new and traditional cyberattacks
Keywords
Internet of Things (IoT), Intrusion Detection System (IDS), Deep Learning, CNN-LSTM, Cybersecurity, Network Security, Edge Computing, Anomaly Detection
How to cite this paper
@article{1709373,
author = {James O. Ogunseye, Afolabi Emmanuel},
title = {Enhancing IoT Network Security Through Deep Learning-Powered Intrusion Detection System},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {1453-1458},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709373.pdf},
abstract = {The explosive growth of the Internet of Things (IoT) has introduced increased connectivity and automation with increased cybersecurity threats, especially on resource-constrained devices. Traditional intrusion detection systems (IDS) cannot respond to these dynamic threats due to their reliance on known attack patterns and lack of adaptability. This work investigates the application of deep learning (DL) techniques, in the form of a hybrid Convolutional Neural Network?Long Short-Term Memory (CNN-LSTM) model, to improve IDS performance for IoT networks. The proposed model was evaluated and tested on three publicly available benchmark datasets?CICIoT2023, Bot-IoT, and UNSW-NB15?and has been shown to have improved accuracy, precision, and recall compared to traditional machine learning models. Despite DL's typically high computational requirements, the model showed efficient performance with minimum latency, and it was a suitable candidate for the edge. The results confirm the potential of DL-based IDS to provide robust, adaptive, and elastic solutions for IoT security against new and traditional cyberattacks},
keywords = {Internet of Things (IoT), Intrusion Detection System (IDS), Deep Learning, CNN-LSTM, Cybersecurity, Network Security, Edge Computing, Anomaly Detection},
month = {June},
}