Whenever an intrusion happens, privacy and security of the system are compromised. In order to detect different types of attacks that happen in a network, Intrusion Detection System (IDS) plays a crucial role in Network security. IDS is designed in order to classify the activities of the system into abnormal and normal. Machine learning based Intrusion Detection is gaining attention in recent years and is able to give better results with greater accuracy and high detection rate on novel attacks. In this paper, performance of different kernels of Support Vector Machine (SVM) are evaluated against Knowledge Discovery in Databases Cup'99(KDD) data set and detection accuracy, detection time are compared. The detection time is reduced by adopting Principal Component Analysis (PCA) which curtails higher dimensional dataset to lower dimensional dataset. The experiments which are conducted in this research shows that Gaussian Radial Basis Function kernel of SVM has higher detection accuracy.