Network Intrusion Monitoring System

Abstract

This web application helps to identify an attack or sense abnormal behaviour in the network and send an alert to the user and protect the user. When the user login into the portal he gets the information about the network’s accuracy, f1-score, precision. This helps the user to detect how safe his network is for the system. Network intrusion detection systems (NIMS) play a critical role in safeguarding computer networks against various cyber threats. Traditional rule-based NIMS often struggle to keep pace with the evolving nature of attacks and the increasing complexity of network environments. In recent years, machine learning (ML) techniques have emerged as a promising approach to enhance the effectiveness of intrusion detection by enabling systems to learn and adapt to new threats. This paper presents an overview of the application of ML techniques in network intrusion detection. We discuss the challenges faced by traditional NIMS and highlight the advantages offered by ML-based approaches, including their ability to detect anomalies, classify network traffic, and adapt to changing attack patterns. We also provide a comprehensive survey of state-of- the-art ML algorithms commonly used in NIMS, such as deep learning, support vector machines, and ensemble methods. Keywords— web application, network intrusion detection, cyber threats, machine learning, anomalies, classification, deep learning.