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In this paper, we evaluate performance of SVM with linear, quadratic, and cubic kernels. The SVM kernels are compared based on accuracy and the F-Score when ...
In this paper, we evaluate performance of SVM with linear, quadratic, and cubic kernels. The SVM kernels are compared based on accuracy and the F-Score when ...
Apr 18, 2019 · In this Thesis, we evaluate the performance of linear, polynomial, quadratic, cubic, Gaussian radial basis function, and sigmoid SVM kernels ...
Anomaly detection categories including ML-based approaches, reachability-based methods, statistical pattern recognition, and validation studies based on ...
Harvard, Batta, p., (2019). 'Evaluation of Support Vector Machine Kernels for Detecting Network Anomalies'. Available at: https://catalog.caida.org/paper/ ...
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Aug 10, 2024 · The Polynomial Kernel SVM demonstrated an accuracy of 99.55% and an F-score of 99.53%. It also showed high precision and recall for many classes ...
This paper reports on application of the support vector machine method to eight real world time series data sets to detect anomalies using three different ...
This paper presents a method based on one class support vector machine (OCSVM) for detecting network anomalies. The telecommunication network performance data ...
Our experiments show the superiority of RSVMs not only in terms of high intrusion detection accuracy and low false positives but also in terms of their their ...
Jun 5, 2023 · The authors aimed to detect anomalies using one class of samples trained by the PSO method. The evaluation is performed on real network traffic ...