Abstract-Intrusion detection is an effective mechanism to deal with challenges in network security. The rapid development in networking technology has raised the need for an effective intrusion detection system (IDS) as traditional intrusion detection methods cannot compete against the newly advanced intrusion attacks. With increasing number of data being transmitted daily to/from a network, the system needs to detect intrusion in such a large data traffic quickly and reliably. Thus, the application of data mining/machine learning comes handy to identify such unusual attacks in an efficient and effective manner. In this paper, we propose an intrusion detection system based on outlier detection, which is an instance of data mining. We deploy a sequence of operations of principal component analysis (PCA), k-means clustering, and single-class support vector machine (SVM) in order to model the normal network traffic instances. We then feed the new instances to the resultant model to predict whether they are normal or attacks. We have applied our model to the KDD Cup '99 dataset and achieved promising results.
In REF , the authors combine several machine learning techniques to obtain an effective intrusion detection mechanism.
Intrusion Detection Based On Outlier Detection Method