@inbook{0a91e9d6b12b43b9ae5b00169560b085,
title = "A Classified Method Based on Support Vector Machine for Grid Computing Intrusion Detection",
abstract = "A novel ID method based on Support Vector Machine (SVM) is proposed to solve the classification problem for the large amount of raw intrusion event dataset of the grid computing environment. A new radial basic function (RBF), based on heterogeneous value difference metric (HVDM) of heterogeneous datasets, is developed. Two different types of SVM, Supervised C\_SVM and unsupervised One\_Class SVM algorithms with kernel function, are applied to detect the anomaly network connection records. The experimental results of our method on the corpus of data collected by Lincoln Labs at MIT for an intrusion detection system evaluation sponsored by the U.S. Defense Advanced Research Projects Agency (DARPA) shows that the proposed method is feasible and effective.",
author = "Qinghua Zheng and Hui Li and Yun Xiao",
year = "2004",
doi = "10.1007/978-3-540-30208-7\_127",
language = "英语",
isbn = "3540235647",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "875--878",
editor = "Hai Jin and Jianhua Sun and Yi Pan and Nong Xiao",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}