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.