The Impact of Machine Learning on Cybersecurity in Social Networks

In recent years, the proliferation of social networks has significantly increased the volume and sensitivity of user-generated content, making these platforms attractive targets for cyber threats. Traditional cybersecurity mechanisms have proven inadequate in detecting sophisticated and evolving attacks such as phishing, malware distribution, and identity theft. Machine Learning (ML) has emerged as a powerful tool in enhancing cybersecurity by enabling systems to learn from historical data, identify patterns, and predict malicious activities in real-time. This study investigates the impact of ML techniques—including Supervised Learning (SL), Unsupervised Learning (UL), and Reinforcement Learning (RL)—on improving cybersecurity in social networks. We explore how ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), and Deep Neural Networks (DNN) can be applied to detect anomalies, spam, and coordinated attacks with high accuracy. The integration of ML into Intrusion Detection Systems (IDS) and User Behavior Analytics (UBA) is also examined. The findings indicate that ML not only enhances threat detection capabilities but also reduces false positives and response time. This research highlights the critical role of ML in the development of intelligent, adaptive, and proactive security frameworks for social networking platforms.

کلیدواژه ها:

Machine Learning ، User Behavior Analytics ، Deep Neural Networks ، Intrusion Detection Systems ، Social Networks ، Reinforcement Learning