Published Paper Details:

Phishing, a prevalent cybercrime, involves deceiving individuals into disclosing personal or confidential information under the guise of legitimate websites or emails. Recognizing phishing websites is challenging due to their similarity to authentic ones. Our study focuses on employing machine learning techniques for efficient phishing website detection. We detail the approach encompassing data gathering, preprocessing steps, extracting features, and employing Support Vector Machine (SVM) for classification. SVM stands out due to its capacity to handle high-dimensional data and non-linear relationships, making it robust to overfitting. SVM offers understandable detection capabilities by optimizing the margin between classes to its maximum extent. Our research aligns with broader cybersecurity objectives, aiming to safeguard individuals and organizations against online deception. Through the development of robust detection systems, we contribute to enhancing cybersecurity measures and empowering users with more secure online experiences. This endeavor underscores the importance of proactive measures in combatting evolving cyber threats