Supervised Learning Cybersecurity

Supervised Learning for Cybersecurity

This skill helps you implement supervised machine learning algorithms for cybersecurity applications. It provides ready-to-use Python code for common security tasks like intrusion detection, malware classification, phishing detection, and spam filtering.

Quick Start

Choose your task and algorithm, then run the corresponding script:

# For intrusion detection with Random Forest
python scripts/train_intrusion_detection.py --algorithm random_forest

# For phishing detection with Logistic Regression
python scripts/train_phishing_detection.py --algorithm logistic_regression

# For comparing multiple algorithms
python scripts/compare_algorithms.py --dataset nsl-kdd

Available Algorithms

Common Cybersecurity Tasks

1. Intrusion Detection (NSL-KDD Dataset)

Detect network attacks from connection features.

python scripts/train_intrusion_detection.py --algorithm random_forest

What it does:

• Loads NSL-KDD dataset (train/test)
• Encodes categorical features (protocol_type, service, flag)
• Trains model to classify normal vs attack traffic
• Outputs accuracy, precision, recall, F1, ROC AUC

Expected results: Random Forest typically achieves 75-80% accuracy, 95%+ precision, 60-65% recall on NSL-KDD.

2. Phishing Website Detection

Classify websites as phishing or legitimate.

python scripts/train_phishing_detection.py --algorithm svm

What it does:

• Loads Phishing Websites dataset from OpenML
• Trains model on URL/domain features
• Outputs probability scores for phishing likelihood
• Evaluates with classification metrics

Expected results: SVM and Gradient Boosting typically achieve 95%+ accuracy, 98%+ ROC AUC.

3. Spam/Email Classification

Use Naive Bayes for text-based classification.

python scripts/train_spam_detection.py --algorithm naive_bayes

What it does:

• Processes email text features
• Uses Gaussian or Multinomial Naive Bayes
• Fast training and prediction
• Good baseline for text classification

Algorithm Selection Guide

Choose Logistic Regression when:

• You need calibrated probability outputs
• Interpretability matters (feature coefficients)
• Dataset is large (scales well)
• Decision boundary is approximately linear

Choose Decision Trees when:

• You need human-readable rules
• Transparency is critical for security operations
• You want to understand which features trigger alerts
• Dataset has mixed numeric/categorical features

Choose Random Forests when:

• You want robust, out-of-the-box performance
• You need to reduce overfitting from single trees
• You have structured/tabular data
• You want feature importance scores

Choose SVM when:

• You have high-dimensional features
• Decision boundary is non-linear
• Dataset size is moderate (<100k samples)
• You need maximum margin separation

Choose Naive Bayes when:

• You're classifying text (spam, phishing emails)
• Speed is critical (real-time filtering)
• You have many features with independence assumption
• You need a fast baseline

Choose k-NN when:

• Dataset is small (<50k samples)
• You want example-based explanations
• Decision boundary is irregular
• You can afford slower prediction time

Choose Gradient Boosting when:

• You need the highest possible accuracy
• You have structured/tabular data
• You can tune hyperparameters
• You're willing to trade interpretability for performance

Choose Linear Regression when:

• You're predicting continuous values (not classification)
• You need to estimate numeric outcomes (e.g., attack volume, risk scores)
• Relationship is approximately linear

Evaluation Metrics Explained

• Accuracy: Overall correctness (TP+TN)/(TP+TN+FP+FN)
• Precision: Of predicted attacks, how many are real? TP/(TP+FP)
• Recall: Of real attacks, how many did we catch? TP/(TP+FN)
• F1-Score: Harmonic mean of precision and recall
• ROC AUC: Threshold-independent measure (1.0 = perfect, 0.5 = random)

For cybersecurity:

• High recall is critical for intrusion detection (catch all attacks)
• High precision reduces analyst fatigue (fewer false alarms)
• Balance based on your threat model and operational constraints

Ensemble Methods

Combine multiple models for better performance:

python scripts/train_ensemble.py --method stacking

Voting Ensemble: Multiple models vote on final prediction

• Simple, robust
• Reduces individual model errors

Stacking: Meta-model learns to combine base model predictions

• Often achieves best performance
• More complex to implement

Data Preprocessing Checklist

Before training any model:

1. Handle missing values - Fill or remove
2. Encode categorical features - LabelEncoder or one-hot
3. Scale numeric features - StandardScaler (required for SVM, k-NN, Logistic Regression)
4. Split train/test - Use stratify for balanced classes
5. Check class balance - Consider SMOTE or class weights if imbalanced

Common Issues and Solutions

Problem: Model overfits (high train accuracy, low test accuracy)

• Solution: Reduce model complexity (max_depth, n_estimators), add regularization, use cross-validation

Problem: Model underfits (low accuracy on both train and test)

• Solution: Increase model complexity, add features, try different algorithm

Problem: Class imbalance (many more normal than attack samples)

• Solution: Use class_weight parameter, SMOTE oversampling, or focus on recall/F1 instead of accuracy

Problem: Slow training time

• Solution: Use Random Forest instead of SVM, reduce n_estimators, use subset of data, enable parallel processing (n_jobs=-1)

Problem: Poor recall (missing attacks)

• Solution: Adjust classification threshold lower, use recall-optimized metrics, try ensemble methods

Next Steps

1. Start with a baseline - Logistic Regression or Random Forest
2. Evaluate thoroughly - Check all metrics, not just accuracy
3. Compare algorithms - Use compare_algorithms.py to test multiple models
4. Tune hyperparameters - Use GridSearchCV or RandomizedSearchCV
5. Consider ensembles - Combine models for best performance
6. Monitor in production - Track performance drift over time

References

• NSL-KDD Dataset: https://github.com/Mamcose/NSL-KDD-Network-Intrusion-Detection
• Phishing Websites Dataset: OpenML ID 4534
• scikit-learn Documentation: https://scikit-learn.org
• XGBoost: https://xgboost.readthedocs.io
• LightGBM: https://lightgbm.readthedocs.io