deeppacket

Modern computer networks generate huge volumes of traffic every second. Identifying abnormal or malicious behavior such as scanning, traffic floods, or suspicious connection patterns is difficult using manual monitoring or simple rule-based systems. Traditional tools often detect threats too late or rely only on fixed signatures, which fail against new or unknown attack patterns. There is a need for an intelligent, real-time system that can analyze network traffic behavior, learn what “normal” looks like, and automatically detect anomalies and potential attacks without depending only on predefined signatures. This project aims to build a smart network monitoring and anomaly detection platform that captures packet data, converts it into flow-level features, applies machine learning models along with heuristic rules, and alerts administrators through a live dashboard. The goal is to improve early threat detection, reduce manual analysis effort, and provide explainable risk scoring for suspicious traffic sources.