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Machine Learning-Based Network Intrusion Detection Optimization for Cloud Computing Environments
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DOI：10.1109/TCE.2024.3458810.png)
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摘要
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Cloud computing is an emerging choice among businesses all over the world since it provides flexible and world wide Web computer capabilities as a customizable service. Because of the dispersed nature of cloud services, security is a major problem. Since it is extremely accessible to intruders for any kind of assault, privacy and security are major hurdles to the on-demand service's success. A massive increase in network traffic has opened the path for increasingly difficult and broad security vulnerabilities. The use of traditional Intrusion Detection Systems (IDS) to prevent these attempts has proven ineffective. Therefore, this paper proposes a novel Network Intrusion Detection System (NIDS) based on a Machine Learning (ML) model known as the Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) techniques. Furthermore, the hyperparameter optimization technique based on the Crow Search Algorithm is being utilized to optimize the NIDS' performance. Besides, the XGBoost-based feature selection technique is used to improve the classification accuracy of NIDS's method. Finally, the performance of the proposed system is evaluated using the NSL-KDD and UNR-IDD datasets, and the experiment results show that it performs better than baselines and has the potential to be used in modern NIDS.
Keyword:
Support vector machines
Machine learning algorithms
Accuracy
Feature extraction
Clustering algorithms
Training
Security
Cloud computing
machine learning
network intrusion detection
performance optimization
期刊
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10.9
论文数:
5.3K
被引数:
6.8K
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分享收藏An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier使用混合特征选择方法和ML分类器的云入侵检测系统的改进设计.png)
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IEEE ACCESSIF3.6
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