# Detecting Phishing Domains Using Machine Learning
###### Abstract  
Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator's (URL's) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature.  
phishing detection; machine learning; phishing domains; artificial neural networks; support vector machine; decision tree; random forest +
Footnote †: journal: _Applied_  
attacks were listed in 2016, a 65% increase from the previous year [8]. The frequency of these attacks erodes consumers' trust in social works, such as webpages.  
There are various types of web fraud [9], and phishing websites are a common entry point for online social engineering attempts. To start, the hacker creates a webpage by impersonating a reputable website. They then send these fishy URLs to potential victims via spam chats, messages, or social media sites, hoping that unsuspecting users will believe it is a real URL [10]. If users enter their personal information (bank account numbers, government savings numbers, and so on) at the link sent by the hacker, that data will be compromised.  
There are a lot of strategies to combat phishing [11]. Artificial intelligence (AI) has had a huge impact on almost every industry, including cybersecurity because AI can detect spam, phishing, spear phishing, and other assaults using past attacks in the form of datasets.  
This paper develops and compares the effectiveness of machine learning (ML) classification models in detecting phishing domains. The goal is to improve detection by using the most accurate model of the four to predict if a webpage is a phish or legal. A phished domain is difficult to analyze and comprehend since it involves social and technical issues for which there is no one-size-fits-all analysis. As a result, all phishing domain causes and features were analyzed quantitatively and qualitatively to determine where to focus the model to better decrease the danger arising from a visit to a phishing website, particularly regarding consumer trust.  
The rest of this paper is organized as follows: Section 2 reviews the latest research on phishing attacks. The data used, and the solutions based on ML classifiers are presented in Section 3. The proposed model architecture is presented in Section 4. Section 5 includes the results and evaluation of the proposed model. Finally, Section 6 presents the paper's conclusion and directions for future work.