Intrusion Detection System using Machine Learning Project
Several supervised learning models were evaluated including KNearest Neighbors, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression. The Random Forest model achieved the highest test accuracy of 99.45% in classifying network traffic by attack type. The most important input features were connection duration, service, source bytes etc. The fitted model was saved to make predictions on new data records. An interactive demo was created to allow entering a sample traffic data instance and getting the predicted attack type. The report covers end-to-end steps of exploratory analysis, data preprocessing, model building, evaluation, selection and use. The Random Forest model showed excellent performance in detecting intrusions for the NSL-KDD dataset. In future work, model optimization techniques like hyperparameter tuning, advanced ensemble methods, and feature engineering can potentially improve accuracy further. Deployment considerations like cloud hosting, incremental learning and monitoring are discussed to transition the model to real-world usage. Estimating the business impact by reducing costs and security risks through intrusion detection creates additional value.
This project aims to contribute to the advancement of IDSs by amalgamating traditional and contemporary techniques, yielding a resilient system capable of adapting to evolving threat landscapes. The endeavor underscores the importance of comprehensive dataset characteristics and thorough model evaluation in the pursuit of effective intrusion detection.
Intrusion Detection System and detect Network Attacks using Machine Learning
Network intrusion detection is an increasingly critical component of cybersecurity defense systems. With growing cyber threats, organizations need robust intrusion detection to identify anomalous traffic, block malicious attacks in real-time, and prevent security breaches. This report demonstrates applying machine learning techniques to classify network traffic data for accurate and automated intrusion detection.
Intrusion detection systems (IDS) monitor network activity to detect violations and threats. Traditional IDS relied on rule-based approaches with signatures to detect known attacks. However, these fail to generalize for new threats. Modern IDS leverage machine learning to train models on network data that can detect both existing and zero-day attacks. This data-driven approach builds intelligent systems that continually improve as they see more data.
Modern IDS should leverage machine learning to automatically learn patterns from traffic data to detect intrusions. With sufficient training data, ML models can identify malicious activity without relying on predefined signatures or coded rules. They can uncover complex relationships in traffic features that indicate attacks. Models like deep neural networks can detect anomalies and low prevalence threats unseen in training.
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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