Year: 2025
Type: article
Abstract: Malicious URLs and websites continue to undermine online security, with search engines inadvertently becoming platforms for fraudulent sites. Traditional phishing detection methods often based on white lists, blacklists, or single-model approaches fail to address the evolving sophistication of phishing attacks. This persistent threat highlights the urgent need for more advanced and adaptive security measures that can reliably identify unsafe URLs in real time. Motivated by these challenges, our research introduces an enhanced phishing detection framework that integrates Natural Language Processing (NLP) with a combination of machine learning algorithms specifically, Support Vector Machine (SVM), Random Forest, and Decision Tree. The SVM algorithm is chosen for its robustness in handling high-dimensional data, while Random Forest and Decision Tree contribute through ensemble learning and interpretability, respectively. Together, these methods form a comprehensive system that accurately differentiates between malicious and legitimate URLs. Additionally, the system incorporates AES encryption to secure sensitive user data, ensuring that browsing history and other critical information remain confidential. The significance of this research lies in its dual contribution to improving cybersecurity and safeguarding user privacy. By effectively detecting phishing attempts and encrypting user data, our approach not only mitigates the risks associated with malicious URLs but also establishes a higher standard for protecting sensitive information online. This integrated solution paves the way for more resilient defences against phishing attacks, offering users enhanced security without compromising privacy.
Authors: Mr. K. Thangadurai ME, R. Divya Sri, K. Nivetha, S. Anu, D. Akalya
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410271981