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Network Intrusion Detection System

Nowadays, network technologies are essential for transferring and storing information of companies, industries, and users. Although, transferring information rate expands the attack surface and offers a rich environment to intruders. In this project, a Network IDS that incorporates a signature-based IDS is proposed to detect known attacks. We will train datasets i.e. CICIDS-2017 and Botnet by using Decision trees, Random Forest, and Extra trees. The effectiveness of the proposed system is calculated by F1-score, accuracy, and precision.

Keywords: Machine Learning
Tools: Python
Department: Department of Computer Science

Project Team Members

Name Email
Mahnoor Awan mahnoor2018@namal.edu.pk

Project Poster

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