## 1 Introduction  
In recent years, computer security issues, such as cyberattacks, data intrusions, and other forms of malicious activities, have increased significantly. This trend is influenced by a number of factors, including a growing dependence on technology, an evolving threat landscape, human error, an absence of cybersecurity awareness, increased connectivity, etc. [1]. Phishing is a type of cyber attack in which attackers attempt to trick users into disclosing sensitive information, such as usernames, passwords, and credit card details, by posing as a trustworthy entity [2]. Numerous methods, including email, social media, instant messaging, and SMS, can be used to carry out phishing attacks. These attacks are designed to deceive users into revealing sensitive information, such as login credentials, credit card information, or other personal data, which can then be used for fraudulent purposes. A successful phishing attack can have severe consequences, including financial losses, identity theft, and reputation damage. In addition to all this, phishing attacks can be carried out to deliver malware or ransomware attacks. The Anti-Phishing Working Group (APWG) conducted research for the 2 nd Quarter 2023 Phishing Activity Trends Report, focusing on the increasing rate of phishing attacks [3]. The findings indicate a notable increase over the past four years, with a growth rate exceeding 150% annually, as illustrated in Figure 1. According to Proofpoint's 2023 State of the Phish Report--Phishing Stats and Trends, 84% of the organizations experienced at least one successful phishing attack. These successful attacks mainly target the private information of companies, leading to increasing financial losses [4]. As seen from this, phishing is still one of the most prevalent and difficult-to-prevent attack types, as attackers constantly change their methods and find ways to circumnavigate security measures. However, there are methods to reduce the risk of phishing attacks, such as educating users and implementing technical controls such as spam filters, URL-based phishing detection [5], content-based phishing detection [6], two-factor authentication [7], etc. The URL-based phishing detection system, which analyzes features derived from URLs such as URL length, use of HTTPS, and domain age, are highly effective in identifying and preventing phishing attacks [8]. This system quickly analyzes and recognizes patterns, making it a popular choice for detecting deceptive URLs that appear legitimate but direct users to fraudulent websites. In recent years, Machine Learning (ML)-based algorithms have gained popularity as a means of protecting computer systems against phishing attacks by automatically identifying malicious traffic. Phishing attacks are difficult to detect and prevent due to the use of sophisticated deception techniques by attackers and the variety of communication channels [9]. Large datasets containing legitimate and phishing can be analyzed using ML algorithms to identify distinguishing patterns and characteristics. These algorithms can use related patterns to identify potential phishing attacks, even if they have never been seen before. Over time, ML can also improve the accuracy and efficiency of phishing detection systems. As new phishing attacks are identified, ML algorithms can update their models to detect future attacks of similar nature. Big data lead to longer training times in ML algorithms due to the increased computational costs associated with processing larger datasets. Not only the number of samples in the dataset but also its dimensionality affects the time. The high dimensionality of the dataset extends the inference time of ML systems, particularly because of the increased cost of feature extraction. The inference time is vital in real-time phishing detection models because it directly influences users and impairs the run-time performance of the deployed ML models.