Advancements in Machine Learning for Network Anomaly Detection: A Comprehensive Investigation

Weishuo Xu

2024

Abstract

This study examines the progress made in Machine Learning (ML) techniques for identifying network abnormalities, which is crucial for ensuring cybersecurity in the modern day. At first, anomaly detection depended on partially automated, rule-based techniques, which were restricted by the constantly changing cyber threats and the intricate nature of networks. The incorporation of artificial intelligence has completely transformed detection methods, employing machine learning to improve precision and effectiveness. The exploration focuses on supervised learning models, namely Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Random Forests. It emphasizes that these models heavily depend on large labeled datasets. In order to overcome this obstacle, the research explores unsupervised and semi-supervised methods that can detect new attacks even without labeled data. In addition, the study focuses on deep learning and reinforcement learning due to their sophisticated abilities in recognizing patterns and adapting to new information. The review identifies specific issues such as the reliance on huge datasets, the need for significant computational resources, and the desire for models that can be easily understood. It recommends that future research should prioritize the development of machine learning models that are adaptive, efficient, and interpretable for the purpose of detecting network anomalies.

Paper Citation

in Harvard Style

Xu W. (2024). Advancements in Machine Learning for Network Anomaly Detection: A Comprehensive Investigation. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 585-589. DOI: 10.5220/0012959700004508

in Bibtex Style

@conference{emiti24,
author={Weishuo Xu},
title={Advancements in Machine Learning for Network Anomaly Detection: A Comprehensive Investigation},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={585-589},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012959700004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Advancements in Machine Learning for Network Anomaly Detection: A Comprehensive Investigation
SN - 978-989-758-713-9
AU - Xu W.
PY - 2024
SP - 585
EP - 589
DO - 10.5220/0012959700004508
PB - SciTePress