# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 1 Introduction  
The extensive proliferation of emails poses an increasing and dangerous menace. One of the most harmful tactics used by cybercriminals to carry out destructive activities is phishing attacks via email. This type of attack is considered the first step in launching more extensive and complex attacks, such as Advanced Persistent Threats (APTs) and Exploit Kits [1]. Although phishing email attacks have persisted for a long time, conventional email filtering methods have proven ineffective in successfully countering such attacks. According to recent phishing studies, 75% of organizations experienced a phishing assault in 2020, and 96% of these attacks were carried out using emailis [2]. Since COVID-19 occurred in this period, cybercriminals have taken advantage of people's desire to know more about this pandemic to trick them into clicking on harmful websites or files. The phishing attempts associated with COVID-19 were highly successful in tricking victims intothinking they were authentic sources by posing as governments, health centers, ministries of health, or other prominent organizations in a relevant country [2]. Phishing emails have been studied by the academic community for over a decade, during which researchers have suggested several approaches to identify and prevent phishing email attacks, mostly using machine learning techniques. Conversely, recent studies by [3; 4; 5] have pointed out several holes as well as drawbacks in the existing literature. These drawbacks comprise a limited range of classifiers, which is particularly pertinent in addressing the imbalanced nature of phishing detection, necessitating extensive utilization of balanced datasets. Additionally, there are inadequate evaluation metrics and dataset-related challenges, such as limited data sources for research. The drawbacks above hinder the practicality of the current works in real-world situations. Additionally, to extract important features, earlier machine learning models focused mostly on either the email's structural properties (headers, URLs, grammar, and attachments) or its body text (text analysis and natural language processing). Phishing detection is now known as a crucial area of cybersecurity, and many industry-standard products, like Microsoft Defender, Google Safe Browsing, and PhishTank, offer fundamental protections against these attacks. These tools often use behavior analysis, signature-based techniques, and blacklist databases to detect and stop phishing attempts. However, these methods frequently fail to identify novel or subtle phishing variations that evade conventional filters. In response to this challenge, we suggest using ensemble learning (EL) which is considered one of the most popular approaches used for handling these drawbacks. The application of EL involves the integration of multiple machine learning algorithms, and has shown significant advancements in various research domains, including detecting phishing web pages [6] and fraud detection. Table 1 presents the evolution of phishing attacks over the years. This study is motivated by the fact that phishing attacks are becoming more sophisticated, making many of the detection methods that are now in use obsolete, such as algorithms and strategies employed by security systems, browsers, email providers, and other entities to detect and block phishing attacks. The limitations found in our literature review emphasize that phishing attempts are still a danger and that detection techniques are continually being developed. EL can effectively utilize all the information from the text-based features of ML algorithms. Among these algorithms is the one with the best performance using text-based features and the highest classification results. As a result, this improves phishing email detection performance. In light of the aforementioned observation, the objective of this paper is to tackle the following research question: Does the amalgamation of EL with text-based attributes enhance the comprehensive efficacy of detecting phishing emails?