# Detecting Phishing Domains Using Machine Learning
## 3 Related Work
The authors of [43] suggested a new method based on both URLs as inputs and HTML-related data. After the features were extracted, a stacking strategy merged the learners. The researchers then ran tests on a variety of datasets, including 2000 webpages taken from Phishtank (1000 legitimate and 1000 phishing sites). The second dataset came from Alexa and contained nearly 50,000 websites. To improve their accuracy, they used SVMs, NNs, DT, and RF, which they combined through stacking. This study obtained a high level of accuracy using a variety of classifiers. The authors of [44] looked at how stacking techniques could be used to identify phishing websites. The goal of these tests was to enhance precision metrics using PCA and stacking the most efficient classifiers. Other classifiers using proposed features N1 and N2 outperformed stacking (RF, NN, stowing). The tests were carried out using datasets from phishing websites. With 11,055 web pages, the dataset had 32 preprocessed characteristics. Another strategy is the extra-tree base classifier utilized by the authors of [45], who used it to classify several meta models: AdaBoost, bagging, rotation forest, and LogitBoost-Extra Tree. The suggested models outperformed current ML-based phishing attack detection models, and, as a result, the authors recommended using meta-algorithms to create phishing attack detection models. To improve the detection of phishing websites, the authors of [46] suggested a phishing detection model based on a particle swarm optimization (PSO) algorithm. Their proposed method used PSO to weigh distinct websites, resulting in increased accuracy for classifying abnormal phishing websites. PSO weighting distinguishes different aspects of a website, considering how important they are in detecting phishing from legitimate websites. According to the findings, their proposed PSO-based component weighting improved the ML model's ability to recognize and monitor both phishing and legitimate websites individually. The authors of [47] employed an evolutionary neuro-fuzzy intelligence system-based resilient approach with integrated features to identify and guard against phishing attacks. The authors of [48] introduced the PhishBench benchmarking structure, which permits researchers to evaluate the characteristics of phishing attacks and fully comprehend different evaluation circumstances, unified framework specifications, data, machine learning algorithms, and evaluation metrics. When the proportion of phishing and authentic traffic fell from one to 10, the classification execution was reduced. In terms of the F1 score, the drop in execution ranged from 5.9% to 42%. An intelligent phishing website identification method was proposed by [PERSON] and [PERSON] [49]. They used proprietary machine learning approaches to differentiate phishing websites. Several classifier approaches were applied to create a reliable and intelligent phishing detection system. The performances of their ML approaches were evaluated using ROC area, F-measure, and AUC. With a 97.61% accuracy, Adaboost with SVM outperformed all other classification approaches. Alternatively, [PERSON] et al. [50] developed a learning-based technique for determining page design comparability, which might be utilized to identify phishing attack pages. They built a phishing classifier using dual ML algorithms, a support vector machine, and a decision tree for effective page layout aspects. They used genuine website page testing from phishtank.com and alexa.com to validate their methodology. [PERSON] et al. [51] employed a dataset from the University of California at Irvine's machine learning repository, which had 2456 unique URLs and more than 11,000 URLs, with 6157 phishing and 4898 normal URL. They took 30 characteristics from the URLs and utilized them to forecast attacks. They employed DT, RF, gradient boosting, generalized linear, and PCA as machine learning techniques. [PERSON] and [PERSON] [52] employed the SMOTE approach to increase their model's detection coverage.