ARCHIVES

Phishing attacks are a significant threat to internet security, most commonly attacking users using spoofed websites. The study "Phishing Website Detection Based on URL Features" seeks to leverage machine learning algorithms for the detection of phishing sites through identifying specific URL features. The research determines the effectiveness of various feature selection techniques and demonstrates that the Random Forest classifier yields the highest accuracy rate of 98.23% with the lowest rate of false positive. Based on URL features, the proposed model aims to enhance detection capability, thereby providing an effective defense mechanism against phishing attacks. This approach not only returns to the field of cybersecurity but also offers practical solutions for safeguarding individuals and organizations against committing or falling victim to online fraud. "Phishing Website Detection Based on URL Features Using Deep Learning" discusses the application of advanced deep learning techniques to enhance the detection of phishing websites. This paper employs a full data set of phishing and regular URLs, and from them various features are extracted, including structural features and semantic properties of URLs. Employing a deep learning framework with Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the model is trained to identify patterns that indicate phishing attacks. The results demonstrate an outstanding improvement in detection accuracy with more than 90% true positive rate and minimal false positives. The research demonstrates the strength of deep learning methods in combatting phishing attacks and represents a useful tool for safeguarding users from cyber deception. Phishing is a criminal technique used to deceive individuals into sharing confidential data, such as passwords and credit card numbers, by presenting itself as a trustworthy entity. Phishing website detection based on URL characteristics without relying on content analysis or blacklists is the current project. By examining structural, lexical, and statistical characteristics of URLs, the system predicts whether a website is genuine or phishing. The model employs machine learning algorithms to offer an efficient and scalable solution to counter phishing attacks, and artificial intelligence was employed for fake Prediction