Evaluation of machine learning based optimized feature selection approaches and classification methods for Intrusion Detection System
MB Musthafa, MA Ali, S Huda, Y Kodera… - 2023 International …, 2023 - ieeexplore.ieee.org
2023 International Conference on Consumer Electronics-Taiwan (ICCE …, 2023•ieeexplore.ieee.org
An Intrusion Detection System (IDS) is used in the field of cyber security to prevent and
mitigate threats during transmitting data between the devices and server. An IDS using
Machine Learning (ML) can be applied to identify and classify security threats. Various ML
techniques have been applied to improve the model performance of IDS. In addition, the
quality of the dataset is also an important determinant that can greatly improve the detection
accuracy. The authors propose an IDS framework based on SVM ensemble with feature …
mitigate threats during transmitting data between the devices and server. An IDS using
Machine Learning (ML) can be applied to identify and classify security threats. Various ML
techniques have been applied to improve the model performance of IDS. In addition, the
quality of the dataset is also an important determinant that can greatly improve the detection
accuracy. The authors propose an IDS framework based on SVM ensemble with feature …
An Intrusion Detection System (IDS) is used in the field of cyber security to prevent and mitigate threats during transmitting data between the devices and server. An IDS using Machine Learning (ML) can be applied to identify and classify security threats. Various ML techniques have been applied to improve the model performance of IDS. In addition, the quality of the dataset is also an important determinant that can greatly improve the detection accuracy. The authors propose an IDS framework based on SVM ensemble with feature selection and balancing dataset. The experiment results show that the proposed model can achieve a high detection performance.
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