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Accuracy Determination: An Enhanced Intrusion Detection System Using Deep Learning Approach
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Author(s): Rithun Raagav (Vellore Institute of Technology, India), P. Kalyanaraman (Vellore Institute of Technology, India)and G. Megala (Vellore Institute of Technology, India)
Copyright: 2023
Pages: 13
Source title:
Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT
Source Author(s)/Editor(s): P. Swarnalatha (Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India)and S. Prabu (Department Banking Technology, Pondicherry University, India)
DOI: 10.4018/978-1-6684-8098-4.ch018
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Abstract
The internet of things (IoT) links several intelligent gadgets, providing consumers with a range of advantages. Utilizing an intrusion detection system (IDS) is crucial to resolving this issue and ensuring information security and reliable operations. Deep convolutional network (DCN), a specific IDS, has been developed, but it has significant limitations. It learns slowly and might not categorise correctly. These restrictions can be addressed with the aid of deep learning (DL) techniques, which are frequently utilised in secure data management, imaging, and signal processing. They provide capabilities including reuse, weak transfer learning, and module integration. The proposed method increases the effectiveness of training and the accuracy of detection. Utilising pertinent datasets, experimental investigations have been carried out to assess the proposed system. The outcomes show that the system's performance is respectable and within the bounds of accepted practises. The system exhibits a 97.51% detection ability, a 96.28% reliability, and a 94.41% accuracy.
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