Developed a Real-time Intrusion Detection System for Windows that leverages Machine Learning techniques to identify and prevent network intrusions. The system uses a Supervised learning model, Random Forest, to detect known attacks from CICIDS 2018 & SCVIC-APT databases, and an Unsupervised learning model, Autoencoder, for anomaly detection.