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All Journal International Journal of Advanced Science Computing and Engineering
Ansari, Mohammad Sadique
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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A Cutting-Edge Deep Learning Method for Enhancing IoT Security Ansar, Nadia; Ansari, Mohammad Sadique; Sharique, Mohammad; Khatoon, Aamina; Malik, Md Abdul; Siddiqui, Md Munir
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.37

Abstract

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.
Co-Authors Ansar, Nadia Khatoon, Aamina Malik, Md Abdul Sharique, Mohammad Siddiqui, Md Munir