Monitoring Incident Response Using Real-Time Analytics

In today's digital landscape, rapid and effective incident response is crucial for maintaining organizational security against sophisticated cyber threats. This project explores the optimization of incident response processes through the implementation of advanced data analytics, machine learning, and time series forecasting algorithms. The system utilizes historical incident data along with advanced algorithms like ARIMA, Support vector machine, LSTM, KNN, Facebook Prophet, Decision Tree, and Random Forest to predict incident volumes, prioritize tickets, and evaluate RFC outcomes. By combining these diverse predictive models, the system improves forecasting accuracy and reliability, offering valuable insights that aid strategic decision-making in incident management. The findings highlight the significant role that advanced analytics and machine learning play in streamlining incident response, improving operational efficiency, and ultimately contributing to a more robust cybersecurity posture for organizations. This research offers valuable recommendations for leveraging these technologies to enhance the effectiveness of incident response strategies.