Performance Analysis of Intrusion Detection System using CNN, ANN and DNN

DOI

10.2991/978-94-6463-250-7_23How to use a DOI?

Keywords

—NIDS (Network Intrusion Detection System); CNN; DNN; ANN; DL; Cyber Security

Abstract

The Network Intrusion Detection System (NIDS) is a crucial aspect of safeguarding against cyber threats in the domain of cybersecurity. The major drawback of conservative approaches like Machine Learning (ML) that involve manual feature selection is their dependency on human involvement, which can hinder their efficacy. Deep learning (DL) is one of the technologies widely used in intrusion detection systems, it increases the performance of the model and securing the networks and classify the attacks. The primary concern regarding both convergence and speed along with the uneven values of the input-hidden layer were addressed as a gap in NIDS. This research compares the Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Artificial Neural Network (ANN) are all related to the field of neural networks. The Performance is evaluated using the following metrics like Accuracy, Precision, Recall and true positive rate. For evaluating the effectiveness of the proposed model in both binary and multiclass classifications, the benchmark dataset CSE-CIC-IDS2018 is utilized. As per the experimental findings, the CNN model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.72%.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. C. Jeyavim Sherin
AU  - K. Parkavi
PY  - 2023
DA  - 2023/10/17
TI  - Performance Analysis of Intrusion Detection System using CNN, ANN and DNN
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 125
EP  - 130
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_23
DO  - 10.2991/978-94-6463-250-7_23
ID  - JeyavimSherin2023
ER  -