Instagram Cyber Incident Monitoring Platform

The rapid growth of social media platforms has increased the prevalence of cybersecurity threats such as phishing, malware propagation, spam campaigns, and coordinated cyber-attacks. Instagram, being one of the most widely used social networking platforms, has become a potential target for malicious activities. This paper presents an Instagram Cyber Incident Monitoring Platform that automatically detects and classifies cybersecurity threats using a hybrid detection framework. The proposed system combines heuristic-based filtering with machine learning techniques to improve detection accuracy and efficiency. Public Instagram data including captions, posts, and profile metadata are collected and preprocessed using text normalization techniques. TF-IDF is employed for feature extraction, while a Random Forest classifier performs threat classification into categories such as phishing, malware, DDoS, and safe content. The system further assigns severity levels to detected threats and visualizes results through an interactive dashboard. Flask is used for backend processing, React for frontend visualization, and MongoDB for data storage. Experimental evaluation demonstrates the effectiveness of the proposed approach in accurately identifying cyber threats while maintaining scalability and real-time analytical capabilities. The platform provides an intelligent solution for proactive cybersecurity monitoring in social media environments.