# Detecting Phishing Domains Using Machine Learning
## 3 Related Work  
Users, overall, tend to overlook a website's URL. This makes them more likely to fall prey to a phishing domain, which might otherwise be avoided by determining whether a URL is authentic. Unfortunately, traditional methods for detecting phishing attacks have limited accuracy and can only detect roughly 20% of attempts. ML techniques for phishing detection produce better results, but they are time-consuming, even on small databases, and they are not scalable. Furthermore, heuristics-based phish detection has a significant false-positive rate. Previous research on anti-phishing models has concentrated on strategies to modify efficiency. Even so, feature reduction and the use of an ensemble model can improve these models' accuracy even further. For phishing domain detection, machine learning algorithms are prevalent, and using them has become a straightforward categorization problem. The data at hand must have properties relevant to phishing and legitimate website classes to build an ML-based detection model. Previous works have shown that when robust machine-learning approaches are utilized, detection accuracy is high. To reduce features, a variety of feature selection strategies are applied. To train a machine learning model to predict phishing attacks versus legal traffic, a batch of data is given as the input. Dataset visualization becomes more efficient and intelligible when characteristics are reduced. The DT, C4.5, k-NN, and SVM algorithms are the most important classifiers; they have been utilized in numerous research projects, and they have detected phishing attacks with the greatest accuracy and efficiency. According to the empirical experiment's findings, manual parameter adjustment, protracted training  
Figure 6: A deep neural network used for phishing detection [35]. periods, and poor detection accuracy are prevalent problems with modern deep learning systems [36]. Despite these benefits, researchers have noted the limits of their studies. Many pointed out that ensemble learning techniques have not been applied and that feature selection and reduction have not been performed. A range of strategies has been applied to combat phishing attacks. One paper [37] used different classifiers, such as naive Bayes and SVM. Similarly, the authors in [38] utilized random forest to differentiate phishing attacks from normal websites. [PERSON] et al. [17] reported that their proposed classifiers were extremely effective at classifying phishing websites. They reported that random forest was the most accurate classifier, at 97.26%. The authors of [39] proposed a paper concentrating on feature selection in phishing websites. They sorted the characteristics into six groups using the UCI dataset, which has more than 11,000 URLs and 30 characteristics. They chose three groups and decided that these were the best solutions for detecting phishing attacks accurately. [PERSON] et al. [40] suggested three strategies for detecting phishing websites. The first entailed assessing various URL attributes; the second determined the validity of the website by determining where it was hosted and who managed it; and the third method determined the authenticity of the website through visual, appearance-based analysis. They used ML methodologies and algorithms to assess the numerous aspects of the URLs and websites. [PERSON] et al. [41] used a binary classifier based on an RF algorithm and a feature selection algorithm based on the relieff algorithm. They utilized data from the Mendeley domain as the source for their feature selection algorithm. They then used the selected features to train an RF algorithm to predict phishing attacks. The work of [PERSON] et al. [42] employed three ensemble learning strategies: bagging, boosting, and stacking. Their dataset had 30 characteristics and 5126 records in the result column. The data comes from UCI, which is open to the public. They integrated their classifiers to achieve the highest level of accuracy possible from a DT.