Semi-supervised classification for intrusion Detection System in networks

We propose a semi supervised classifier for intrusion detection. In our approach, we classify the data entering the computer network. To achieve this, we start with two broad classes of data namely, malicious data and good data. We use Support vector machine based classifier with spherical decision boundaries to classify a chosen subset of malicious data taken as training samples. In the Intrusion Detection System (IDS) database, all data identified as malicious data according to our classifier is included as signature (of attack). Using our classifier for testing the out-of-sample data samples, we observe that the accuracy of the system is 72% for web log data.