Detection of malicious web pages by evolutionary ensemble learning

Abstract

Nowadays, World Wide Web is an absolutely necessary and inevitable part in the life of all people all over the world which they are using Internet online services routinely to save and manage their sensitive information. On the other hand, the attackers' tendency is increasing to set up web-based client-side attacks. Because of the easiness of making these type of attacks, efficient and accurate detection of malicious web pages is one of the most important and critical strategies in the network security domain. In this paper, we present a model for detection of malicious web pages based on ensemble learning. Our major purpose of applying the idea of ensemble in the learning process is to provide more learning chance to the data instances, which are misclassified by previous classifiers. Therefore, it causes to reduce the classifiers error rate and finally leads to improve the malicious web pages classification accuracy. For doing this, we propose an ensemble approach which uses evolutionary reasoning to achieve improvement of classification accuracy in the detection of malicious web pages. We assign weight to the base classifiers and use an optimization technique based on genetic algorithm to select the best committee members of classifiers to make an optimal ensemble for classification of web pages. The obtained results from the implementing of our proposed ensemble method with different base classifiers illustrate that this algorithm leads to the classification accuracy improvement in all test cases. The most improvement is related to using SVM as the base classifier, which was 12.5%. Moreover, the maximum classification accuracy belongs to the proposed ensemble method using REPTree as the base classifier and it was 95.38%.