Design and Implementation of Machine Learning-Based Network Intrusion Detection
Keywords:
Network intrusion detection, Cyber security, Support vector machine, ensemble learningAbstract
Systems for detecting intrusions are vital to network security and are necessary for maintaining network integrity. To improve the effectiveness of machine learning techniques, ensemble learning has been frequently used. Additionally, the quality of training data has a significant impact on detecting abilities. Marginal density ratios have consistently outperformed the powerful unilabiate classifiers. In this paper, we recommend a system for detecting intrusions that is based on groups of SVMs and has been functionally enhanced. Our method involves transforming the original characteristics to marginal density logarithmic thresholds in order to produce new, enhanced, and modified training data. The creation of an intrusion detection model then takes place using an SVM-Set. The system employs a number of machine learning techniques, including ensemble learning, to enhance detection performance. Raising the caliber of training data also involves the use of feature augmentation. Using an ensemble of Support Vector Machine (SVM) models, the suggested approach creates an effective intrusion detection framework. The effectiveness of the proposed design was assessed using simulations and the data base CICIDS2017, which simulates network traffic in the real world. The results of the experiment were compared to earlier studies, and it was found that the precision of binary and multiclass categorization had increased. Another illustration of the efficiency of the model was the high level of precision of the restored transportation system.
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