Comprehensive Survey of Deep Learning-Based Intrusion Detection and Prevention Systems for Secure Communication in the Internet of Things
Keywords:
Internet Of Things, Intrusion Detection System, Malicious, Reliability, SecurityAbstract
The Internet of Things (IoT) is a gadget that is connected to many devices that are permitted to detect and collect data from the provided environment and communicate the data over wireless media without any human involvement. IoT is widely used in various applications, namely healthcare, education, agriculture, military applications, etc. Due to its open nature and design, it is vulnerable to various kinds of network attacks. In an IoT environment, security should be stronger in order to prevent malicious activities. The Intrusion Detection System (IDS) was created to identify and prevent various types of harmful assaults on the network. Every organisation needs to manage these kinds of attacks and malicious activities. Many organisations fail to detect and prevent many unknown malicious attacks. So, safeguard measures have to be taken by the organisation in order to provide better security, reliability, and privacy in the IoT environment. In this paper, a survey on security issues related to the methods of deep learning and machine learning was used to analyse vulnerabilities in the IDS network.
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