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A Systematic Literature Review of Network Intrusion Detection System Models

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Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics (PCCDA 2023)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Rapid growth in the communication and Internet domains requires large network data and network size. Thereby, in such a network, several new attacks are being produced and have possessed challenges for effective detection of intruders to protect network security. Network intrusion detection system (NIDS) acts as a mechanism that prohibits the potential intrusions in network by continuously monitoring network traffic analysis to provide network confidentiality, availability, and its integrity. Various researchers NIDS model based on deep learning and machine learning still faces potential limitations in reducing false alarm rate and testing of models against updated datasets. Recently, deep learning and machine learning-based systems are mainly used as probable methodologies to effective detection of intrusions. This paper provides systematic literature review of several latest research papers by defining recent advancements and trends of DL- and ML-based methodologies. In addition, by consideration of several literature research papers drawbacks, we recommend distinct potential challenges and future scopes in enhancing ML- and DL-based NIDS.

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Authors and Affiliations

  1. Department of Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, India

    Yogesh & Lalit Mohan Goyal

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  1. Yogesh
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  2. Lalit Mohan Goyal
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Correspondence to Yogesh .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Dr. B. R. Ambedkar National Institute Technology, Jalandhar, India

    Anupam Yadav

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India

    Satyasai Jagannath Nanda

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Meng-Hiot Lim

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yogesh, Goyal, L.M. (2023). A Systematic Literature Review of Network Intrusion Detection System Models. In: Yadav, A., Nanda, S.J., Lim, MH. (eds) Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics. PCCDA 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4626-6_38

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