Intrusion Detection System in Cyber Security
Network intrusion detection is critical for protecting systems against cyber threats. This report presents a machine learning approach for classifying network traffic as normal or attack types using the NSL-KDD dataset. The data contains records of simulated network connections with 43 features and labels for normal or 24 attack categories grouped into 4 classes – Denial of Service, Probe, User to Root, and Remote to Local.
Exploratory data analysis was performed to understand the distribution of the label, protocol, service, duration and other features. This provided insights like common attacks and frequently used protocols/services. Bivariate analysis identified relationships between variables like associations between protocols and attack classes. The data was pre-processed by encoding categorical variables and splitting into train and test sets for modeling.
Network Intrusion Detection System using Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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