An approach for efficient intrusion detection for KDD dataset: a survey
Namita Sharma and Bhupesh Gaur
Abstract
Identifying possible attacks on the network system is a challenging task. There is several research works are processed in this direction, but the need of improvement is always remains in the research. The accuracy of detection is the major challenge today. It becomes tougher as the intrusion types and their nature are different. So considering the above event detail discussion and analysis has been presented in this paper. Analysis of different techniques like data mining, machine learning and optimization is also presented so that the significant improvement may be judges and better future suggestion can be came out of this. DOS, U2R, R2L and probe attacks are considered and the Knowledge Discovery and Data mining (KDD) database have been considered for comparison of different techniques.
Keyword
Intrusion detection, Data mining, Machine learning, Optimization, KDD, DOS, U2R, R2L and probe.