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Internet worms are malware programs that imitate themselves and spread around the network. Internet worm, a wide spreading malcode exploits vulnerability in the operating system, hard disk, software and web browsers. This paper analyzes and classifies the Internet worm, depending on the training signatures. This work presents the Internet worm detection mechanism, using Principal Component Analysis (PCA) and Support Vector Machine (SVM). A Selective sampling technique is applied to maximize the performance of the classifier and to reduce misleading data instances. The results obtained show improved memory utilization, detection time and detection accuracy for Internet worms.