Anomaly Detection using Support Vector Machine

Support vector machine are among the most well known supervised anomaly detection technique, which are very efficient in handling large and high dimensional dataset. SVM, a powerful machine method developed from statistical learning and has made significant achievement in some field. This Technique does not suffer the limitations of data dimensionality and limited samples. In this present study, We can apply it to different domains of anomaly detection. Support vectors, which are critical for classification, are obtained by learning from the training samples. Results of SVM achieved high Accuracy and low false positive rate. Theoretically we compared our approach with neural network and clustering technique