Phishing is a type of cybersecurity attack that involves stealing personal information such as passwords, credit card numbers, etc. To avoid phishing scams, we have used Machine learning techniques to detect Phishing Websites. Therefore, in this paper, we are trying to find the total number of ways to find Machine Learning techniques and algorithms that will be used to detect these phishing websites. We are using different Machine Learning algorithms such as KNN, Naive Bayes, Gradient boosting, and Decision Tree to detect these malicious websites. The research is divided into the following parts. The introduction represents the focused zone, techniques, and tools used. The Preliminaries section has details of the preparation of the information that is required to move further. Later the paper emphasizes the detailed discussion of the sources of information. This research addresses the imperative need for advanced detection mechanisms for the of phishing websites. For this purpose, we explore state-of-the-art machine learning, ensemble learning, and deep learning algorithms. Cybersecurity is essential for protecting data and networks from threats. Detecting phishing websites helps prevent fraud and safeguard personal information. To evaluate the efficacy of our proposed method, the top features using information gain, gain ratio, and PCA are used to predict and identify a website as phishing or non-phishing. The proposed system is trained using a dataset that covers 11,055 websites. The ensemble learning model applied achieved an impressive 99% accuracy in predicting phishing websites, surpassing previous models, and setting a new benchmark in the field. The findings highlight the effectiveness of combining deep learning architectures with ensemble learning, offering not only improved accuracy but also adaptability to emerging phishing techniques. His research focuses on the detection of phishing websites using machine learning techniques. The proposed approach analyzes various characteristics of a website, including features extracted from the URL, domain registration information, and selected webpage content. These features help capture meaningful patterns that can differentiate phishing websites from legitimate ones. Several supervised machine learning algorithms are implemented and trained on a phishing website dataset to automatically learn these patterns. The models are then tested and compared to evaluate their ability to correctly identify phishing websites. The performance of the machine learning models is measured using standard evaluation metrics such as accuracy, precision, recall, and F1-score. The experimental results show that machine learning–based models are highly effective in detecting phishing websites, with ensemble-based classifiers providing the best overall performance. These models are able to detect not only known phishing websites but also newly created and previously unseen phishing websites, which is a key advantage over traditional detection techniques. The proposed phishing detection system is efficient, scalable, and suitable for real-time implementation. It can be integrated into web browsers, email filtering systems, or other cybersecurity tools to provide early warnings to users and reduce the risk of phishing attacks. This research demonstrates that machine learning offers a practical and reliable solution for improving phishing website detection and strengthening online security. In recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyberworld. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the “zero-day” attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyze the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate[3].
Phishing Website Detection Machine Learning Algorithms (MLA), Cybersecurity, URL Analysis, Feature Extraction, Classification Models, Supervised Learning, Random Forest, Support Vector Machine (SVM), Decision Tree, Logistic Regression, Data Preprocessing, Ensemble Learning, Accuracy and Precision, False Positive Rate, Web Security.
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