Intrusion Detection System using Machine Learning ( CICIDS 2017 Dataset )

Intrusion Detection Systems (IDS) are important today when network security has become a priority, the systems help to identify and prevent cyber threats. Traditional IDS mechanisms, like signature-based detection, however, face difficulties detecting new, or previously unseen, attacks. For this reason, machine learning (ML) techniques, using data patterns to detect anomalies and guaranteeing real time network protection, have been adopted.

Network Attacks necessitates robust Intrusion Detection Systems (IDSs) for safeguarding digital networks. This project delves into the development of an IDS using the CICIDS2017 dataset—a comprehensive benchmark dataset that addresses inherent shortcomings in prior datasets. The dataset encompasses diverse network configurations, labeled traffic, and realistic attack scenarios, ensuring a holistic evaluation framework.

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Software Requirements :-
• Coding Language : Python
• Implementation: Web Framework.
• Operating system : Windows 10 / 11.
• Graphical User Interface : HTML, CSS, Django

Hardware Requirement :-
• Input Devices : Keyboard, Mouse.
• System : Pentium i3 Processor.
• Hard Disk : 500 GB.
• Ram : 4 GB.

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