Network Intrusion and Detection System using Machine Learning

Abstract

In todays digital world, networks are constantly exposed to a variety of security threats, including unauthorized access, malware, and Distributed Denial of Service (DDoS) attacks. Traditional security measures often fall short in detecting and preventing these evolving threats in real-time. This project proposes a Network Intrusion Detection and Prevention System (NIDPS) that leverages machine learning algorithms to identify and mitigate network intrusions effectively. The system uses a dataset of network traffic to train and validate a model capable of distinguishing between benign and malicious activities. Key machine learning models, such as Random Forest, Support Vector Machine (SVM), and Neural Networks, are evaluated based on performance metrics like accuracy, precision, recall, and F1 score. The proposed solution is designed to operate in real-time, deploying preventive measures upon detecting potential threats to secure the network from future attacks. This project aims to enhance network security by providing a robust, adaptable, and scalable intrusion detection and prevention solution. Keywords: Network Intrusion Detection, Machine Learning, Network Security, Intrusion Prevention System, Anomaly Detection, Cybersecurity, Real-Time Monitoring, Network Traffic Analysis, Threat Detection, Data Security