Open AccessIntrusion Detection System using KDD Cup 99 Dataset
Author(s) -
Ch. Aishwarya,
N. Venkateswaran,
T. Supriya,
V. Sreeja
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d2017.029420
Subject(s) - c4.5 algorithm , computer science , naive bayes classifier , intrusion detection system , correctness , data mining , random forest , the internet , machine learning , artificial intelligence , computer security , support vector machine , world wide web , programming language
Intrusion Detection System is a vital feature of protecting network infrastructure from unauthorized users or hackers. Intrusion detection system is used to identify several types of malicious activities that could effect the safety of network and to reduce network traffic. Because of faster growth of Internet, networks are growing rapidly in every area of society. As a result, large amount of data is travelling across many networks which may lead to vulnerability of integrity and confidentiality of data. Many Machine learning models are opened up providing new opportunity to classify traffic in network. In quest to select a good learning model, this paper illustrates performance between J48, Naive Bayes and Random forest classification models. The KDD Cup 99 dataset is used for experimental analysis to identify which classification model improves correctness of data and attains highest accuracy.