Deep Learning Towards Intrusion Detection System (IDS): Applications, Challenges and Opportunities

Authors

  • Selvam Ravindran Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu 603203, Tamil Nadu, India
  • Velliangiri Sarveshwaran Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu 603203, Tamil Nadu, India

DOI:

https://doi.org/10.13052/jmm1550-4646.1958

Keywords:

Internet of Things, Machine Learning, Deep Learning, Optimization, Intrusion Detection

Abstract

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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Author Biographies

Selvam Ravindran, Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu 603203, Tamil Nadu, India

Selvam Ravindran completed Bachelor in Computer Science and Engineering from Muthayammal Engineering College, Rasipuram, Anna University, Chennai. Master in Computer Science and Engineering from Kumaraguru College of Technology, Anna University, Coimbatore. I am pursuing a Doctor of Philosophy at SRM University Kattankulathur Campus, Chennai. I have 11 years of Experience as an Assistant professor in the Computer Science and Engineering Department for various Engineering Colleges, I published 1 International Journal and 5 more National conferences.

Velliangiri Sarveshwaran, Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu 603203, Tamil Nadu, India

Velliangiri Sarveshwaran obtained his bachelor’s degree in computer science and engineering from Anna University, Chennai. Master in Computer Science and Engineering from Karpagam University, Coimbatore, and Doctor of Philosophy in Information and Communication Engineering from Anna University, Chennai. Currently, he is working as an assistant professor at the SRM Institute of Science and Technology, Kattankulathur Campus, Chennai. He was a member of the Institute of Electrical and Electronics Engineers (IEEE) and the International Association of Engineers (IAENG). He has been serving as reviewer of IEEE Transactions, Elsevier, Springer, Inderscience, and other reputed Scopus-indexed journals. He is specialized in network security and optimization techniques. He published in more than 60 international journals and presented at more than 10 international conferences. He also serves as a technical program committee chair and conference chair at many international conferences. He served as book series editor of “Artificial Intelligence for Sustainability” in CRC Press. He also serves as an area editor for the EAI Endorsed Journal of Energy Web and an academic editor in the Journal of Wireless Communication and Mobile Computing.

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Published

2023-08-14

How to Cite

Ravindran, S. ., & Sarveshwaran, V. . (2023). Deep Learning Towards Intrusion Detection System (IDS): Applications, Challenges and Opportunities. Journal of Mobile Multimedia, 19(05), 1299–1330. https://doi.org/10.13052/jmm1550-4646.1958

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Section

Articles