Anomaly Detection and Intrusion Prevention Using ML in Python

This project focuses on developing an intrusion detection system (IDS) using ML techniques. An IDS is a software tool designed to continuously monitor network traffic to identify malicious activities or violations of security policies. In this study, Python has been utilized to implement the ML models and configure the detection system based on specific design requirements. There are various types of IDS, such as network-based intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS), each serving distinct purposes in enhancing cybersecurity. The system is also capable of identifying anomalies, thereby helping in understanding and mitigating hacker activities. Multiple sensors have been integrated within the IDS to generate and track security events effectively. Additionally, a control console has been used to manage the detection process, which is essential for implementing robust security protocols. A thorough literature review has been conducted, including summaries and research questions, to support this work. The methodology section of the project covers the philosophical assumptions, research questions, and considerations for ensuring the validity and reliability of the study. The methods for data collection and selection have been explained in detail, providing a comprehensive approach to gathering the necessary information. Limitations of the project have also been discussed to present a realistic perspective. The dataset used for this work was sourced from a reliable website and applied to evaluate the accuracy of intrusion detection models. The results section presents the outcomes along with screenshots that highlight the performance of the system. This research was implemented using Python in the Jupyter Notebook environment. The artefact’s development process has been clearly described, outlining its purpose and how it contributes to the overall goal of improving intrusion detection. The methodology further explores how the accuracy score was computed, specifically using a RF classifier, which proved effective in predicting and identifying intrusions. Finally, recommendations have been provided to offer insights and suggestions for enhancing the system’s efficiency. This project not only demonstrates the use of ML for intrusion detection but also serves as a guide for future improvements in network security.