A Cutting-Edge Deep Learning Method For Enhancing IoT Security

There have been significant issues given the IoT, with heterogeneity of billions of devices and with a large amount of data. This paper proposed an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Ne...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Ansar, Nadia, Ansari, Mohammad Sadique, Sharique, Mohammad, Khatoon, Aamina, Md Abdul Malik, Md Munir Siddiqui
Format: Article
Language:English
Subjects:
Artificial neural networks
Communications traffic
Cybersecurity
Deep learning
False alarms
Heterogeneity
Internet of Things
Intrusion detection systems
Machine learning
Real time
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Summary:There have been significant issues given the IoT, with heterogeneity of billions of devices and with a large amount of data. This paper proposed an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious. The real-time processing capability, scalability, and low false alarm rate in our model surpass some traditional IDS approaches and, therefore, prove successful for application in today's IoT networks. The development and the performance of the model, with possible applications that may extend to other related fields of adaptive learning techniques and cross-domain applicability, are discussed. The research involving deep learning for IoT cybersecurity offers a potent solution for significantly improving network security.
ISSN:2331-8422