Enhancing Social Network Security: A Dynamic Approach for Detecting and Mitigating Social Spam

In today’s connected world, social networks have become integral to daily life, but their popularity also exposes them to misuse, such as spam and malicious content. The consequences of social spam go beyond annoyance and can distort discussions and lead to misunderstandings. Therefore, identifying spam-initiating accounts has become an important research concern. This article presents a two-step method that uses specialized data mining techniques tailored for business (Biz) data. The approach dynamically identifies users engaged in spam activities. In the initial phase, a comprehensive user database is created within the Facebook network, identifying potentially anomalous nodes and forming patterns associated with spam behavior. These patterns are used to evaluate new nodes for comparison. When a node is identified as a potential spam source, appropriate actions are taken for effective categorization and management. The effectiveness of our approach is validated through extensive comparative analysis against existing spam detection methods. Empirical results demonstrate a significant improvement in accuracy using our method, highlighting its superiority in detecting and mitigating social spam and addressing a critical challenge in social network security.