Fuzzy c-Means Sub-Clustering with Re-sampling in Network Intrusion Detection

摘要：

Both supervised and unsupervised learning are popularly used to address the classification problem in anomaly intrusion detection. The classical and challenging task in intrusion detection is how to identify and classify new attacks or variants of normal traffic. Though the classification rate is not at par with supervised approach, unsupervised approach is not affected by the unknown attacks. Inspired by the success of bagging technique used in prediction, the study deployed similar re-sampling strategy by splitting the training data into half. Data was obtained from KDDCup 1999 dataset. The finding shows that re-sampling improves performance of Fuzzy c-Means sub-clustering.

作者: Anazida Zainal Den Fairol Samaon Mohd Aizaini Maarof Siti Mariyam Shamsuddin

作者单位: Faculty of Computer Science and Information Systems,Universiti Teknologi Malaysia,81310 Skudai,Johor,Malaysia

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 683-686

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）