Version 1
: Received: 26 March 2024 / Approved: 26 March 2024 / Online: 27 March 2024 (00:16:15 CET)
How to cite:
Mundstock, I. A.; Santo, Y.; Silveira, T. L. T. D.; Immich, R.; Riker, A.; Dalmazo, B. L. On Feature Selection Techniques for Detecting DoS Attacks with a Multi-class Classifier. Preprints2024, 2024031635. https://doi.org/10.20944/preprints202403.1635.v1
Mundstock, I. A.; Santo, Y.; Silveira, T. L. T. D.; Immich, R.; Riker, A.; Dalmazo, B. L. On Feature Selection Techniques for Detecting DoS Attacks with a Multi-class Classifier. Preprints 2024, 2024031635. https://doi.org/10.20944/preprints202403.1635.v1
Mundstock, I. A.; Santo, Y.; Silveira, T. L. T. D.; Immich, R.; Riker, A.; Dalmazo, B. L. On Feature Selection Techniques for Detecting DoS Attacks with a Multi-class Classifier. Preprints2024, 2024031635. https://doi.org/10.20944/preprints202403.1635.v1
APA Style
Mundstock, I. A., Santo, Y., Silveira, T. L. T. D., Immich, R., Riker, A., & Dalmazo, B. L. (2024). On Feature Selection Techniques for Detecting DoS Attacks with a Multi-class Classifier. Preprints. https://doi.org/10.20944/preprints202403.1635.v1
Chicago/Turabian Style
Mundstock, I. A., André Riker and Bruno L. Dalmazo. 2024 "On Feature Selection Techniques for Detecting DoS Attacks with a Multi-class Classifier" Preprints. https://doi.org/10.20944/preprints202403.1635.v1
Abstract
In a hyperconnected society, computer networks play a pivotal role in all activities that permeate our daily lives. In the context of smart cities, theses networks play a pivotal role in enhancing urban efficiency and sustainability. The continuous flow of packets in these networks poses security challenges, from personal data leakage to service unavailability. Anomaly-based methods are commonly used to detect network attacks. In this context, Software-Defined Networking (SDN) emerges as a solution for anomaly detection in network traffic, facilitating comprehensive, real-time analyses and dynamic adaptation to changes. This study aims to present a systematic review of feature selection techniques and evaluate the effectiveness of attribute selection with a multi-class classifier in network anomaly detection. The objective of this paper is to inspire future research and identify trade-offs among these techniques for detecting Denial of Service (DoS) attacks. Based on the findings, the analysis of the entire dataset yields superior results in terms of precision (99%), but using the output of OneR, although resulting in a slight loss of precision compared to the complete dataset, presents the highest precision among other studied techniques, indicating a trade-off between precision and processing time efficiency.
Keywords
Network Security; Feature Selection; Denial of Service Attacks; Systematic Review; Smart City; Anomaly Detection
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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