Open AccessPerformance and Computation Time Enhancement of Various Machine Learning Techniques for NSL-KDD Dataset
Author(s) -
Pradeep Kumar K V,
K. Anusha,
S. Nachiyappan
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f9877.038620
Subject(s) - computer science , intrusion detection system , computation , data mining , machine learning , artificial intelligence , missing data , algorithm
To develop an effective intrusion detection system we definitely need a standardize dataset with a huge number of correct instances without missing values. This would significantly help the system to train and test for real-time use. Previously for research purpose, KDD-CUP’99 dataset has been used, but later on, it has been seen that it is not so useful for training the model as it consists a lot of missing and abundant values. All this issue have been tackled in NSL dataset. To analyze the capabilities of the dataset for intrusion detection system we have analyzed various machine learning classification algorithm to classify the attack over any network. This paper has explored many facts about the dataset and the computation time.