An intelligent CRF based feature selection for effective intrusion detection

As the internet applications are growing rapidly, the intrusions to the networking system are also becoming high. In such a scenario, it is necessary to provide security to the networks by means of effective intrusion detection and prevention methods. This can be achieved mainly by developing efficient intrusion detecting systems that use efficient algorithms which can identify the abnormal activities in the network traffic and protect the network resources from illegal penetrations by intruders. Though many intrusion detection systems have been proposed in the past, the existing network intrusion detections have limitations in terms of detection time and accuracy. To overcome these drawbacks, we propose a new intrusion detection system in this paper by developing a new intelligent Conditional Random Field (CRF) based feature selection algorithm to optimize the number of features. In addition, an existing Layered Approach (LA) based algorithm is used to perform classification with these reduced features. This intrusion detection system provides high accuracy and achieves efficiency in attack detection compared to the existing approaches. The major advantages of this proposed system are reduction in detection time, increase in classification accuracy and reduction in false alarm rates.