# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 2 Related Work
### Identification of Phishing Emails Employing Analysis of Textual Characteristics  
A notable discovery is that earlier studies (before 2015) on detecting phishing emails generally emphasize content-based characteristics, but more current studies prioritize textual characteristics. The current advancement of artificial intelligence (AI) and NLP techniques has significantly improved our ability to identify phishing emails accurately. This progress is mostly due to the use of textual information included inside the emails. In a recent publication by [12], the authors employed the TF-IDF approach to generate text-based features, followed by the utilization of a Graph Convolution Network for email categorization. The study conducted by [PERSON] et al. [13] involved the analysis of a total of 3685 phishing emails and 4894 benign emails. By employing 3-fold cross-validation, the study determined the accuracy rate to be 98.2%. However, the study by [PERSON] [12] presents two main limitations. Firstly, number of phishing emails is significantly lower in comparison to benign ones. Secondly, the authors should have considered a more realistic assessment that involves the deployment of fresh phishing emails. The unique features of the body text of phishing emails were investigated by [PERSON] et al. [14]. Using different feature combinations and machine learning methods, the researchers conducted several trials. With a success percentage of 99.95%, the XGBoost algorithm produced the best outcomes. [PERSON] et al. [14] introduced a multi-stage method for spotting phishing emails, building on their work. This method combines two techniques: feature extraction and feature selection. Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) were employed in the feature extraction phase, while chi-squared statistics and Mutual Information were used in the feature selection process. Utilizing Latent Semantic Analysis (LSA) in conjunction with the XGBoost (version number 2.1.2) algorithm provided the highest level of efficacy. The same dataset was used in both studies to evaluate their approaches, yielding similar findings. However, it is crucial to recognize that both investigations need to have considered the increasing quantity of phishing emails, considering that the phishing emails under scrutiny were out of date. A framework called THEYMIS was created in the study by [PERSON] et al. (2015) with the goal of recognizing phishing emails. This framework makes use of Recurrent Convolutional Neural Networks (RCNN). Word2 Vec, along with character and word levels, were used to separate the emails. Utilizing ([PERSON] et al., 2016), vector sequences were derived from these levels. Using the IWSPA 2018 dataset, THEMIS achieved a 99.848% detection accuracy and a 0.043% false positive rate. In a similar vein, [PERSON] et al. (2017) applied a deep learning (DL) technique to the identification of phishing emails. The authors used a combination of Keras Word Embedding and Convolutional Neural Networks to achieve this. This method's evaluation was carried out using the IWSPA 2018 dataset, accounting for emails with and without headers. The results of the study show that the model performs better when the email headers are ignored, with an accuracy rate of 96.8%. A common limitation between [PERSON] et al. (2017) and [PERSON] et al. (2015) is that they could have evaluated their models' performance on more recent emails to gauge how well they could identify the evolving nature of phishing email attacks. Moreover, [PERSON] et al. (2017) used an unbalanced sample ratio and improperly depended just on the accuracy measure. The SAFE-PC method uses features found in text to recognize phishing emails. Natural language processing (NLP) approaches including synonym substitution, Freebase, and Named Entity Recognition (NER) are used to generate these features. The RUS-Boost approach, which combines boosting and data sampling techniques with other machine learning algorithms, is used in the classification process.