Designing Intrusion Detection System Using Python

Abstract

This project focuses on developing a Network-based Intrusion Detection System (NIDS) using Python to enhance real-time cybersecurity defences. The system aims to detect and adapt to evolving cyber threats through advanced monitoring and machine learning techniques. Key objectives include improving protocol monitoring, integrating machine learning for accurate threat detection, and implementing efficient incident logging. The literature review identifies the limitations of existing Python-based NIDS solutions. The project meticulously defines the system's requirements, emphasising real-time monitoring, anomaly detection, and scalability. The development phase uses Python to create functional classes and methods for detection tasks, incorporating advanced techniques for identifying sophisticated threats. The NIDS is validated through rigorous testing, showcasing its effectiveness against simulated attacks using a hybrid approach of signature-based and machine learning algorithms. The project's comprehensive evaluation underscores its efficiency and adaptability, contributing significantly to cybersecurity defence and laying the groundwork for future NIDS advancements.