An Ensemble Framework for Network Anomaly Detection Using Isolation Forest and Autoencoders

In today's digitally interconnected world, securing computer networks against potential threats is of utmost importance integrity and confidentiality of sensitive information are some of the integral concerns when securing the networks from malicious activities and retention of faith in digital infrastructure. For our initiative, we are working on developing an ensemble approach for intrusion detection using isolation forest and autoencoders. This project aims to bring a higher accuracy along with increased efficiency to the anomaly detection systems by adopting the latest methods of machine learning. The ensemble framework will integrate the strengths of isolation forest, known for its ability to identify anomalies in datasets, and auto encoders, which excel in learning representations of complex data. By combining these methods, the goal of our approach is to develop a working system that would successfully monitor network traffic data for signs of any unauthorized activities. The project will involve implementing and optimizing the individual components, designing the ensemble framework, and conducting rigorous testing and evaluation. The expected results comprise a thoroughly documented framework, research findings that contribute to the network security domain, and the possibility of practical applications that can strengthen network defense mechanisms in real-world scenarios.