# Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
## 2 Literature Review  
In this section, we review related works in the field of phishing detection. The deployment of phishing websites is rapidly expanding, prompting the blacklisting of websites to thwart phishing. Because of their capability for coping with phishing websites and attackers' dynamics, ML strategies have attracted attention rapidly in the phishing website diagnosis domain. Induced bias, the weak accuracy of diagnosis, and a high false-alarm rate (FAR) are some of the ML methods' cons. Because of active phishing attempts, a significant requirement exists for new solutions based on ML for diagnosing phishing websites. Three models of learning given the Forest Penalizing Attribute (ForestPA) mechanism were provided by the authors of [25]. Using the power of all features on a given dataset, the ForestPA generates highly effective decision trees using weight assignment and weight augmentation techniques. Website classification in phishing/legal groups basically relies on significant status on the website. Different solutions have been presented for decreasing phishing attacks, although no solution exists for fully solving the issue, which is one of the motivating strategies in the data mining domain. Data mining specifically refers to \"inducing classification laws\". In [26], a novel mechanism of classification is offered and applied to popular phishing website datasets in the UCI repository. In [27], a novel mechanism of association classification (AC) is provided as automatic and synthetic means for developing classification process accuracy levels in the search for malicious websites. In order to ensure the discovery of hidden patterns not produced by AC methods, an intelligent associative classification (IAC) algorithm is proposed. An improved ensemble-based approach is presented in [28] to identify phishing websites. Some ensemble ML methods, such as GradientBoost, LightGBM, RFs, AdaBoost, XGBoost, and bagging, have their parameters optimized by applying genetic algorithms (GAs). After ranking the developed classifiers, the first three models were chosen as basic stacked set classifiers. The UCI phishing dataset was reviewed in [29]. The dimensions of the dataset were reduced, and the effectiveness of the classification systems was compared using a smaller dataset of phishing websites. Since phishing attacks have a few basic characteristics, machine learning is the best option for detecting them. A number of machine learning approaches were used in [30] to detect phishing attacks. There were two priority-based algorithms proposed here. The final fusion classifier was selected based on the output of these algorithms. To achieve high accuracy and reproducible outcomes for phishing website detection, Ref. [31] investigates the problems of developing cost-effective deep learning models and parameter settings. The researchers of [32] proposed a technique for phishing website detection combining feature selection with a nonlinear regression algorithm based on a meta-heuristic algorithm. This paper used a dataset consisting of 11,055 valid and phishing URLs to validate the proposed approach. Then, 20 features were selected for retrieval from the websites. In this work, two feature selection strategies were used to determine the best feature subset: decision trees and packing. The presented regression model parameters were determined by applying the method of HS, and the nonlinear regression strategy was applied for grouping websites. The dynamic pitch adjustment rate employed by the proposed HS algorithm produces new harmonics. The researchers in [33] have proposed a multilayer ensemble learning strategy using estimators at various levels. The present-layer estimators' predictions are presented as input to the next layer. The gradient-boosting paradigm is presented in [34] for issues of classification and regression in ML for small data sources with various sharing. Using Bayesian optimization, the model parameters are gradually adjusted from an initial hypothesis for specific usage scenarios.