International Journal of Computer Applications
Now-a-days, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. New intelligent Intrusion Detection Systems (IDSs) which are based on sophisticated algorithms rather than current signature-base detections are in demand. In this paper, the authors propose a new data-mining based technique for intrusion detection using an ensemble of binary classifiers with feature selection and multi-boosting simultaneously. Their model employs feature selection so that the binary classifier for each type of attack can be more accurate, which improves the detection of attacks that occur less frequently in the training data.