Detect Cyber Intrusion Using Machine Learning

Cyber attacks are increasingly frequent and sophisticated, making intrusion detection systems critical for network security. Machine learning offers a powerful approach to cyber attack detection by learning patterns from network traffic and automatically flagging malicious activities before they cause damage. This technology powers modern security operations centers, protecting everything from corporate networks to critical infrastructure.

In this project, we'll build intrusion detection classifiers using Python, scikit-learn, and the SIMARGL2021 dataset, a publicly available collection of benign and malicious network traffic. We'll start by exploring and preprocessing the dataset with Pandas and NumPy, analyzing features, identifying attack types, and removing redundant data. Using Matplotlib for data visualization, we'll examine the distribution of attack labels and understand class imbalances. We'll then standardize features, encode categorical variables, and split the data into training and testing sets for model evaluation.

We'll train and compare three machine learning classifiers: Random Forest for ensemble predictions, Decision Tree for interpretable rules, and Gaussian Naive Bayes for probabilistic classification. After training each model, we'll test their intrusion detection accuracy on unseen network traffic and compare performance metrics including classification accuracy, precision, recall, and training time. By the end, you'll have a working cybersecurity machine learning system demonstrating scikit-learn classification, network traffic analysis, feature engineering, multi-class classification, and model comparison applicable to any anomaly detection or security monitoring project.