Important Roles Of Data Mining Techniques For Anomaly Intrusion Detection System
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 5
Abstract
Today, there are so many information interchanges are performed in that internet as the increasing the amount of using internet. That why, the methods used as intrusion detective tools for protecting network systems against diverse attacks are became too important. The available of IDS are getting more powerful. But, modern intrusion detection system facing complex problems. These system has to be require reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. In this proposed model, we focus on the best two data mining algorithms for intrusion detection system. The k-Nearest Neighbor was used as the classical pattern reorganization tools have been widely used for Intruder detections. There have some different characteristic of features in building an Intrusion Detection System. Conventional k-NN do not concern about that. Our enhanced Model proposed with an Random Forest (RDF) and k-Nearest Neighbor (kNN) method to perform more accurate classification task of the new model. RDF can select more important features and kNN can select more precisely than the conventional System. Experiments and comparisons are conducted through intrusion dataset: the KDD Cup 1999 dataset.
Authors and Affiliations
Phyu Thi Htun , Kyaw Thet Khaing
Keywords
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