Research Article
Intrusion Detection with Tree-Based Data Mining Classification Techniques by Using KDD
@INPROCEEDINGS{10.1007/978-3-319-73447-7_33, author={Mirza Khudadad and Zhiqiu Huang}, title={Intrusion Detection with Tree-Based Data Mining Classification Techniques by Using KDD}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Data mining Intrusion detection system Decision Tree J48 Hoeffding Tree Rep Tree Random Forest Random Tree KDD dataset}, doi={10.1007/978-3-319-73447-7_33} }- Mirza Khudadad
Zhiqiu Huang
Year: 2018
Intrusion Detection with Tree-Based Data Mining Classification Techniques by Using KDD
MLICOM
Springer
DOI: 10.1007/978-3-319-73447-7_33
Abstract
In the recent time a huge number of public and commercial service is used through internet so that the vulnerabilities of current security systems have become the most important issue in the society and threats from hackers have also increased. Many researchers feel intrusion detection systems can be a fundamental line of defense. Intrusion Detection System (IDS) is used against network attacks for protecting computer networks. On another hand, data mining techniques can also contribute to intrusion detection. The intrusion detection has two fundamental classes, Anomaly based and Misuse based. One of the biggest problem with the anomaly base intrusion detection is detecting a high numbers of false alarms. In this paper a solution is provided to increase the attack recognition rate and a minimal false alarm generation is achieved with the study of different Tree-based data mining techniques. KDD cup dataset is used for research purpose by using WEKA tool.
