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- Title
Feature-Selection-Based DDoS Attack Detection Using AI Algorithms.
- Authors
Raza, Muhammad Saibtain; Sheikh, Mohammad Nowsin Amin; Hwang, I-Shyan; Ab-Rahman, Mohammad Syuhaimi
- Abstract
SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision. However, centralization makes it vulnerable to a variety of attack vectors, with distributed denial of service (DDoS) attacks posing a serious concern. Feature selection-based Machine Learning (ML) techniques are more effective than traditional signature-based Intrusion Detection Systems (IDS) at identifying new threats in the context of defending against distributed denial of service (DDoS) attacks. In this study, NGBoost is compared with four additional machine learning (ML) algorithms: convolutional neural network (CNN), Stochastic Gradient Descent (SGD), Decision Tree, and Random Forest, in order to assess the effectiveness of DDoS detection on the CICDDoS2019 dataset. It focuses on important measures such as F1 score, recall, accuracy, and precision. We have examined NeTBIOS, a layer-7 attack, and SYN, a layer-4 attack, in our paper. Our investigation shows that Natural Gradient Boosting and Convolutional Neural Networks, in particular, show promise with tabular data categorization. In conclusion, we go through specific study results on protecting against attacks using DDoS. These experimental findings offer a framework for making decisions.
- Subjects
DENIAL of service attacks; CONVOLUTIONAL neural networks; ARTIFICIAL intelligence; MACHINE learning; ALGORITHMS
- Publication
Telecom, 2024, Vol 5, Issue 2, p333
- ISSN
2673-4001
- Publication type
Article
- DOI
10.3390/telecom5020017