Intrusion Detection for IoT Network Security with Deep Neural Network-Reference-Cited by-同舟云学术

Intrusion Detection for IoT Network Security with Deep Neural Network

Published:2023-03-08 Issue: Volume: Page:
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Author:

Morshedi Roya¹,Matinkhah S Mojtaba¹,Sadeghi Mohammad Taghi¹

Affiliation:

1. Yazd University

Abstract

Abstract one of the most important challenges of the Internet of Things is security. Today, the Internet of Things has found an important place in information technology and human daily life. One of the main challenges of the Internet of Things is security. One of the common methods to intervene in Internet of Things services is Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks. Therefore, intrusion detection systems or IDSs are currently the main and most complete parts of a network monitoring system. This paper uses the CICIDS 2017 data set to present an intrusion detection model in software-driven Internet of Things networks based on deep neural networks to detect distributed denial of service attacks and several other cyber attacks. In addition, we explored effective deep learning models to represent cyber security knowledge in Internet of Things networks, including CNN, DenseNet, CNN and LSTM hybrid models, and our proposed model.

Publisher

Research Square Platform LLC

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