# A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection
## 1 Introduction  
With the growing number of mobile devices, web applications, and users, as well as the prompt evolution of computing, cyber-security has become critical in recent years [1]. Easy access to the computer networks and Internet applications, the accessibility of high-speed networks, and high-industrial advancements such as the development of 5G and 4G technologies, have comprehensively expanded Internet usage worldwide [2]. In particular, as a result of the recent improvements in information technology and digitalization, several enterprises and companies have shifted their businesses from the physical to digital domain, employing mobile and web applications to decrease physical contact [3, 4]. However, this growth in Internet applications and websites has caused them to become significantly open and accessible to the rest of the world and comes with a significant security risk and a leakage of private data. Because of the easy access to the Internet, a person's digital visibility has extensively increased, creating opportunities for digital thieves and hackers to gain access to private data and credentials. Detecting malicious integrity attacks and protecting transmitted data from eavesdropping attacks are critical to prevent user information from being manipulated or destroyed in the integrated data-driven framework [5]. Such cyber-security breaches leadto severe mental stress and financial losses over time [6]. Malicious websites are considered the means by which the hackers can collect private information from unsuspecting Internet consumers. Malicious websites normally appear to be benign websites and request private information, such as passwords, usernames, and credit card information, or gain access to personal images or crucial data. Such data are stored in a form of data storage and might be exploited to achieve the attackers' goals, such as criminal money transfers, online shopping, harassing the user, or blackmailing. Using forged webpages identical to genuine webpages is a common method to steal users' data and private information [7]. One method of ensuring user security is to use classification techniques to determine whether the accessed website is malicious [8; 9]. A correct classification ensures that the user is informed to not enter his/her data and important information on suspect websites [10; 11]. Recently, machine learning methods have demonstrated exceptional results by using data classification [12; 13]. They are not only used for malicious websites and attack detection, but are also applied in numerous identification, classification, and prediction tasks in a variety of fields, such as stock prediction [14], weather prediction [15], and image processing [16]. The machine learning field has shown much progress and promise, with large amounts of data becoming available, the development of advanced computational models, and advancements in computing power. Traditional machine learning methods begin with building a new model architecture, followed by the initialization of its parameters. The model is then trained with the training set to learn the relationship between the features of the inputs and the actual class outputs. After that, the model can be validated in the training stage by computing its results on a validation set, which is known as the validation stage. A trained model is then tested on unseen instances, and the classification results are obtained to assess its performance. Support Vector Machine (SVM) [17], K-Nearest Neighbors (KNN) [18], Decision Tree (DT) [19], Naive Bayes (NB) [20], and Logistic Regression (LR) [21] are some common machine learning methods. In the literature, some evaluation metrics, such as precision, accuracy, recall, and F1-score, are applied to evaluate the machine learning outputs [22]. These evaluation metrics are mathematically computed based on the confusion matrices, in which true positives, false positives, true negatives, and false negatives are counted.