Malware is one of the most common security threats experienced by a user when browsing web pages. A good understanding of the features of web pages (e.g., internet protocol, port, URL, Google index, and page rank) is required to analyze and mitigate the behavior of malware in web pages. This main objective of this paper is to analyze the key features of webpages and to mitigate the behavior of malware in webpages. To this end, we conducted an empirical study to identify the features that are most vulnerable to malware attacks and its results are reported. To improve the feature selection accuracy, a machine learning technique called bagging is employed using the Weka program. To analyze these behaviors, phishing and botnet data were obtained from the University of California Irvine machine learning repository. We validate our research findings by applying honeypot infrastructure using the Modern Honeypot Network (MHN) setup in a Linode Server. As the data suffer from high variance in terms of the type of data in each row, bagging is chosen because it can classify binary classes, date classes, missing values, nominal classes, numeric classes, unary classes and empty classes. As a base classifier of bagging, random tree was applied because it can handle similar types of data such as bagging, but better than other classifiers because it is faster and more accurate. Random tree had 88.22% test accuracy with the lowest run time (0.2 sec) and a receiver operating characteristic curve of 0.946. Results show that all features in the botnet dataset are equally important to identify the malicious behavior, as all scored more than 97%, with the exception of TCP and UDP. The accuracy of phishing and botnet datasets is more than 89% on average in both cross validation and test analysis. Recommendations are made for the best practice that can assist in future malware identification.