  Network attacks have been very prevalent as their rate is growing
tremendously. Both organization and individuals are now concerned about their
confidentiality, integrity and availability of their critical information which
are often impacted by network attacks. To that end, several previous machine
learning-based intrusion detection methods have been developed to secure
network infrastructure from such attacks. In this paper, an effective anomaly
detection framework is proposed utilizing Bayesian Optimization technique to
tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF),
Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance
of the considered algorithms is evaluated using the ISCX 2012 dataset.
Experimental results show the effectiveness of the proposed framework in term
of accuracy rate, precision, low-false alarm rate, and recall.
