###### Abstract  
Internet users are continually exposed to phishing as cybercrime in the 21 st century. The objective of phishing is to obtain sensitive information by deceiving a target and using the information for financial gain. The information may include a login detail, password, date of birth, credit card number, bank account number, and family-related information. To acquire these details, users will be directed to fill out the information on false websites based on information from emails, adverts, text messages, or website pop-ups. Examining the website's URL address is one method for avoiding this type of deception. Identifying the features of a phishing website URL takes specialized knowledge and investigation. Machine learning is one method that uses existing data to teach machines to distinguish between legal and phishing website URLs. In this work, we proposed a method that combines correlation and recursive feature elimination to determine which URL characteristics are useful for identifying phishing websites by gradually decreasing the number of features while maintaining accuracy value. In this paper, we use two datasets that contain 48 and 87 features. The first scenario combines power predictive score correlation and recursive feature elimination; the second scenario is the maximal information coefficient correlation and recursive feature elimination. The third scenario combines spearman correlation and recursive feature elimination. All three scenarios from the combined findings of the proposed methodologies achieve a high level of accuracy even with the smallest feature subset. For dataset 1, the accuracy value for the 10 features result is 97.06%, and for dataset 2 the accuracy value is 95.88% for 10 features. feature selection; phishing detection; machine learning; correlation; feature elimination +
Footnote †: journal: _Future Internet_  
of phishing websites increased. Meanwhile, according to Phislabs's data [6], during 2019 83.9% of phishing attacked services are financial, email, cloud, payment, and SaaS. According to [PERSON] & [PERSON] [7], no single approach or strategy can detect all phishing websites ideally. The problem is that the website's content is liable to change, and the website's operational lifespan is limited. Machine learning (ML) is a promising and intelligent approach. This approach detects new phishing websites by analyzing a range of indicators, better known as features. Research on this topic attempts to implement several methods and algorithms using machine learning. The Hybrid Ensemble Feature Selection method uses a Cumulative Distribution Function gradient (CDF-g) algorithm to identify the automated feature cut-off rank. Additionally, [PERSON] et al. [8] employ an ensemble technique called function perturbation. Due to this approach, Random Forest (RF) outperformed Naive Bayes (NB), JRip, PART Classifier, and Support Vector Machines (SVM). With hybrid Natural language processing (NLP) and words vector-based features, RF receives the most excellent accuracy rating [9]. Furthermore, [PERSON] [10] uses a way that ranks the features based on the maximum variance using Principal Component Analysis (PCA). Next, [PERSON] applies RF, SVM, and Logistic Regression (LR) machine learning algorithms; the result is RF and SVM make better performance by accuracy. Each of these studies used a unique method for selecting features. Recent dataset comes with several features, and the assumption is some researchers use 50-100% of the total features, with complex computational time. For instance, [PERSON] and [PERSON] [11] used 23 from 48 features, [PERSON] [10] used all the features for 49, [PERSON] et al. [12] used 16 from 30 features, [PERSON] and [PERSON] [13] used 27 from 30 features, and [PERSON] et al. [14] used 15 from 30 features  
This paper's hypothesis is that reducing low-impact features lead to simpler computation with an insignificant decline in the recognition rate for the web phishing dataset.