A novel intrusion detection method based on support vector machines

Security of computers and the networks that connect them is increasingly becoming of great significance. As an effect, building effective intrusion detection models with good accuracy and real-time performance are essential. In this paper we propose a new data mining based technique for intrusion detection using Cost-sensitive classification and Support Vector Machines. We introduced an algorithm that improves the classification for Support Vector Machines, by multiplying in the training step the instances of the underrepresented classes. We have discovered that by oversampling the instances of the anomaly, we are helping the Support Vector Machine algorithm to overcome the soft margin. As an effect, it classifies better future instances of this class of interest.