Improved Machine Learning-based System for Intrusion Detection

DOI

10.2991/978-94-6463-540-9_15How to use a DOI?

Keywords

Network Security; Deep Learning; Intrusion Detection; Multi-Layer Perceptron

Abstract

In addressing the growing cyber threats prevalent in the digital age, this research presents Machine Intrusion Detection System Based on Learning (MLIDS), an innovative system for detecting intrusions that harnesses the power of deep learning through the integration of a Multi-Layer Perceptron (MLP). This study aims to enhance the precision of cyber attack detection while minimizing the occurrence of false positives. The MLP model, meticulously crafted using PyTorch, incorporates multiple hidden layers that effectively capture the intricate patterns and non-linear relationships embedded within network traffic data. Significant advancements, such as the implementation of adaptive learning rate modifications and the application of L2 regularization techniques, have substantially bolstered the model’s ability to generalize across various scenarios. The empirical outcomes of this research are compelling, with MLIDS achieving an impressive detection accuracy of 98.76%, surpassing traditional methods such as Naive Bayes and Single-Layer Perceptrons. These results not only highlight the efficacy of MLIDS but also underscore the transformative potential of deep learning in the realm of cybersecurity.

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  - Jiarui Feng
PY  - 2024
DA  - 2024/10/16
TI  - Improved Machine Learning-based System for Intrusion Detection
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 130
EP  - 136
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_15
DO  - 10.2991/978-94-6463-540-9_15
ID  - Feng2024
ER  -