CyberShield: Practical Analysis of an Intelligent Network Intrusion Detection System Based on Machine Learning

This article introduces the open-source project CyberShield, an intelligent network intrusion detection system based on machine learning. The project uses the UNSW-NB15 dataset, and through data preprocessing, feature engineering, and comparison of multiple models (logistic regression, decision tree, random forest), it achieves accurate identification of malicious traffic and provides a real-time prediction web interface using Streamlit. It aims to solve the problem that traditional rule-based IDS cannot handle complex threats, demonstrating the practical value of combining data science with cybersecurity.

In the digital age, the frequency and complexity of cyberattacks are on the rise, and traditional rule-based intrusion detection systems (IDS) are difficult to cope with. The core objective of the CyberShield project is to build an intelligent system that automatically identifies malicious network traffic. By learning normal and abnormal behavior patterns to detect unknown attacks, it has stronger adaptability and generalization capabilities.

The project uses the UNSW-NB15 dataset, created by the University of New South Wales in Australia. It covers nine attack types (Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode, Worms), reflecting the complexity of the modern network environment and being more practical than KDD Cup99.

The data preprocessing process includes:

• Data cleaning: Handling missing values and outliers
• Feature encoding: Converting categorical variables to numerical values
• Feature scaling: Standardizing numerical features
• Feature selection: Filtering discriminative features through correlation analysis This lays the foundation for model training.

Three algorithms are compared:

1. Logistic Regression: A baseline model with strong interpretability
2. Decision Tree: Captures non-linear relationships but is prone to overfitting
3. Random Forest: Integrates multiple trees, has the best stability and accuracy, and is the final choice Evaluation uses cross-validation with metrics including accuracy, precision, recall, and F1-score; feature importance analysis of Random Forest helps understand attack patterns.

The project's highlight is an interactive web application based on Streamlit. Users can upload traffic data to get real-time detection results, lowering the technical threshold. The tech stack includes:

• Scikit-learn: Machine learning algorithms
• Pandas: Data processing
• Streamlit: Web interface construction The code structure is clear, making it easy to learn and secondary development.

Application Scenarios: Enterprise network monitoring, SOC auxiliary decision-making, cybersecurity education platforms, low-cost solutions for small organizations Improvement Directions: Introduce deep learning (LSTM, Autoencoder) to handle time-series features, implement online learning, enhance encrypted traffic detection, and integrate more data sources.