Deep Learning Towards Intrusion Detection System (IDS): Applications, Challenges and Opportunities
DOI:
https://doi.org/10.13052/jmm1550-4646.1958Keywords:
Internet of Things, Machine Learning, Deep Learning, Optimization, Intrusion DetectionAbstract
With the growth of numerous technological areas, including sensors, embedded computing, broadband Internet access, wireless communications, distributed services, automatic identification, and tracking, the potential for integrating smart objects into our daily activities through the Internet has increased. The Internet of Things (IoT) is the confluence of the Internet and intelligent objects that can converse and cooperate with one another. IoT is a brand-new example that unifies Cyberspace with actual physical objects from various areas, including, business processes, human health, home automation, and environmental monitoring. It intensifies the use of Internet-connected strategies in our regular lives, carrying with it several advantages as well as security challenges. Intrusion Detection Systems (IDS) have been a crucial device for the defence of systems and material schemes for more than 20 years. However, applying traditional IDS techniques was challenging due to the IoT’s inimitable features, like resource-constrained devices and particular protocol stacks and standards. As a result, this survey will focus on various Deep Learning (DL)-based intrusion detection techniques. This study makes use of 50 research papers that focused on different techniques, and a review of studies that used those techniques was given. This research enables categorizing the methods employed for intrusion detection in IoT based on Convolutional Neural Network (CNN)-based methods, Deep Neural Network (DNN)-based methods, Optimization-based methods, and so on. Moreover, the categorization of approaches, published year, the dataset used, tools used, and the performance metrics are measured for intrusion detection in IoT. On the basis of the software used for implementation, performance achievement, and other factors, a thorough analysis was conducted. The conclusion identifies the research gaps and issues in a way that makes it clear why should create an efficient method for enabling efficient enhancement.
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