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Assessment of Network Intrusion Detection System Based on Shallow and Deep Learning Approaches

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Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT (ICETCE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1591))

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Abstract

This extensive review aims to classify the Intrusion Detection System (IDS) and various machine learning and deep learning (ML/DL) approaches used for IDS. The survey also addresses security, which is a concern with the Internet of Things. Several types of intrusion detection systems (IDSs), including shallow and deep learning methods and various learning algorithms to aid intrusion detection, are also categorized. This research expands on Network Intrusion Detection Systems and investigates techniques for improving their performance. It provides a more comprehensive understanding of deep and shallow learning methodologies with their benefits and drawbacks. The study component examines IDS classification, feature extraction techniques, machine learning, deep learning, and examples of how these may be applied. The essence of this review will establish a viable approach to assist professionals in modeling trustworthy and powerful IDS based on real-time requirements. Because the methods of intrusions and cyberattacks in networks are constantly evolving, it attracted the interest of many scholars and industrial professionals. However, cyber specialists struggle to develop an accurate and effective Intrusion Detection System (IDS). In addition, an increasing number of devices has resulted in more complicated network topology, raising security risks. As a result, a lengthy and exhaustive review is indispensable while developing a secure communication system.

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Authors and Affiliations

  1. Central University of Rajasthan, Ajmer, India

    Gaurav Meena,  Babita & Krishna Kumar Mohbey

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  1. Gaurav Meena
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  2. Babita
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  3. Krishna Kumar Mohbey
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Correspondence to Gaurav Meena .

Editor information

Editors and Affiliations

  1. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  2. Myanmar Institute of Information Technology, Mandalay, Myanmar

    G. R. Sinha

  3. Indian Institute of Information Technology Kota, Jaipur, India

    Basant Agarwal

  4. Shobhit University, Gangoh, India

    Tarun Kumar Sharma

  5. SKIT, Jaipur, India

    Pankaj Dadheech

  6. SKIT, Jaipur, India

    Mehul Mahrishi

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Meena, G., Babita, Mohbey, K.K. (2022). Assessment of Network Intrusion Detection System Based on Shallow and Deep Learning Approaches. In: Balas, V.E., Sinha, G.R., Agarwal, B., Sharma, T.K., Dadheech, P., Mahrishi, M. (eds) Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT. ICETCE 2022. Communications in Computer and Information Science, vol 1591. Springer, Cham. https://doi.org/10.1007/978-3-031-07012-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-07012-9_28

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