Biblio

In the era of the ever-growing number of smart devices, fraudulent practices through Phishing Websites have become an increasingly severe threat to modern computers and internet security. These websites are designed to steal the personal information from the user and spread over the internet without the knowledge of the user using the system. These websites give a false impression of genuinity to the user by mirroring the real trusted web pages which then leads to the loss of important credentials of the user. So, Detection of such fraudulent websites is an essence and the need of the hour. In this paper, various classifiers have been considered and were found that ensemble classifiers predict to utmost efficiency. The idea behind was whether a combined classifier model performs better than a single classifier model leading to a better efficiency and accuracy. In this paper, for experimentation, three Meta Classifiers, namely, AdaBoostM1, Stacking, and Bagging have been taken into consideration for performance comparison. It is found that Meta Classifier built by combining of simple classifier(s) outperform the simple classifier's performance.

This paper investigates the possibility of using ensemble learning methods to improve the performance of intrusion detection systems. We compare an ensemble of three ensemble learning methods, boosting, bagging and stacking in order to improve the detection rate and to reduce the false alarm rate. These ensemble methods use well-known and different base classification algorithms, J48 (decision tree), NB (Naïve Bayes), MLP (Neural Network) and REPTree. The comparison experiments are applied on UNSW-NB15 data set a recent public data set for network intrusion detection systems. Results show that using boosting, bagging can achieve higher accuracy than single classifier but stacking performs better than other ensemble learning methods.