A Survey on An Anomaly Detection Improvement in Computer Communication Network using ML Techniques

In today's digital age, guarding network structure from cyber pitfalls is a major concern. Traditional hand-grounded Intrusion Detection Systems (IDS) often struggle to identify new or unknown attacks. This creates a need for smarter and more flexible results. This project introduces an anomaly-grounded Network Intrusion Detection System (NIDS) that uses ML approaches to spot unusual functioning in network traffic. The system analyzes patterns and differences from typical activity to detect implicit intrusions, including Denial-of-Service (DoS) attacks, probe attacks, User to Root (U2R) exploits, and Remote to Local (R2L) breaches in real-time. The system works with preprocessed network datasets like NSL-KDD or CICIDS. It extracts important features and trains supervised machine learning models such as Random Forest, Support Vector Machine, and KNearest Neighbors (KNN) for accurate classification.