Cyber Security Method for Phishing and Malicious Link Detection in Social Meda Using Data Mining Techniques

Phishing attacks and the dissemination of malicious links in social media platforms pose significant threats to users’ security and privacy. In this paper, we propose a novel approach that combines Support Vector Machines (SVM) and Principal Component Analysis (PCA) for phishing and malicious link detection in social media. Our method leverages the power of SVM to classify suspicious links based on various features extracted from social media posts, while PCA is utilized to reduce the dimensionality of the feature space and enhance the efficiency of the detection process. We conducted extensive experiments on a real-world dataset comprising social media posts containing both legitimate and malicious links. The results demonstrate the effectiveness of our approach, achieving high accuracy and low false positive rates. By employing SVM and PCA, our proposed method offers a reliable and efficient solution for protecting social media users from phishing attacks and malicious links, contributing to the enhancement of online security in social media platforms.