# A Heterogeneous Machine Learning Ensemble Framework for Malicious Webpage Detection
###### Abstract
Most of the existing studies simply develop a feature based on the URL or web content and detect it with a machine learning model. These studies are mainly related to phishing site detection [10]. [PERSON] et al. [11] proposed an effective system to detect infections, called Nazca, which detects web requests used for malicious code binary downloads and is designed to work with large-scale networks such as internet service providers (ISP). In this paper, we present an extensive survey of malicious website features and an ensemble machine learning model for malicious web detection. The motivation of this research study includes the following:  
* To propose an advanced machine learning model that can detect and predict the distribution of malicious codes based on URLs and web content without network data analysis. * To predict the risk of distribution by detecting malicious code distribution websites to predict cyber threats in advance, rather than simple malicious web detection. Because their patterns change over time, the information from malicious websites is complex and can be utilized by combining different features. However, the performance of machine learning models differs according to the feature selection techniques. We propose an improved feature set to improve the web detection performance using the collected dataset and feature analysis used in previous studies. Additionally, a module that automatically generates this is included in the framework. In numerous studies, machine learning models, such as support vector machines (SVM), decision trees, and random forests, have already proved their merit. However, machine learning models may provide different results based on generalization and other feature combinations. Thus, we propose an ensemble machine learning method that offers the best performance. In particular, the contributions of this research study include the following:  
* Defining an advanced feature set based on URL and web content and including the most optimized detection model structure. * Providing an improved malicious web detection framework with high accuracy through ensemble techniques based on six different machine learning sub-models. * Providing the performance comparison results of various machine learning models for malicious web detection. * Providing an automated technology for the detection of malicious webpage such as phishing, malicious code distribution, and via the web in the cyber security field. * Reducing cyber security damage by predicting the location of malicious code distribution in advance. The remainder of this paper is organized as follows: In Section 1, we briefly discuss issues related to malicious webpages. Section 2 presents a survey of related works. In Section 3, we describe the methodology for designing ensemble machine learning techniques, including functions and processes. In Section 4, we present the findings of ensemble machine learning testing, including the datasets, as well as a comparison of single and ensemble models. In Section 5, we provide our conclusions and discuss future scope of the research.