Project 011: Network-based Ransomware Detection

Objective

Build a machine learning model that can identify network traffic patterns associated with ransomware activity, distinguishing them from normal, benign traffic through advanced pattern recognition and anomaly detection techniques.

Business Value

- Early Detection: Identify ransomware attacks in progress before file encryption causes irreversible damage

- Automated Response: Enable automated quarantining and incident response workflows

- Network Forensics: Understand unique communication patterns of ransomware for threat intelligence

- Cost Avoidance: Prevent costly ransomware incidents that average $1.85M per attack (IBM Security)

- Proactive Defense: Shift from reactive to proactive ransomware protection

- Compliance Support: Meet regulatory requirements for advanced threat detection

- Business Continuity: Minimize operational disruption through early warning systems

- Risk Mitigation: Reduce cyber insurance costs through demonstrable security controls

Core Libraries

- pandas & numpy: Data processing and numerical computations

- scikit-learn: RandomForestClassifier with balanced class weights for handling imbalanced datasets

- matplotlib & seaborn: Network traffic pattern visualization and confusion matrix analysis

- kaggle: CIC-IDS2017 dataset access containing real WannaCry ransomware traffic samples

Dataset

- Benign Traffic: Monday working hours normal network activity

- Ransomware Traffic: Friday morning captures including WannaCry attack samples

- Features: 80+ network flow characteristics including timing, packet flags, and flow statistics

- Scale: Hundreds of thousands of network flows with extreme class imbalance (ransomware is rare)

- Flow Inter-Arrival Time (IAT) patterns

- Packet flag distributions (URG, PSH, RST flags)

- Flow duration and idle time statistics

- Packet size distributions and byte counts

Step-by-Step Guide

1. Environment Setup and Data Acquisition

pip install pandas numpy scikit-learn matplotlib seaborn kaggle

Set up Kaggle API credentials and download the CIC-IDS2017 dataset containing both benign and ransomware network traffic samples.

2. Data Preprocessing and Class Balance Handling

# Load benign and ransomware traffic files
df_benign = pd.read_csv('Monday-WorkingHours.pcap_ISCX.csv')
df_ransomware = pd.read_csv('Friday-WorkingHours-Morning.pcap_ISCX.csv')
# Handle extreme class imbalance using downsampling
df_majority_downsampled = df_majority.sample(n=len(df_minority)*5, random_state=42)

3. Feature Engineering and Data Cleaning

# Clean column names and handle infinite values
df.columns = df.columns.str.strip()
df.replace([np.inf, -np.inf], np.nan, inplace=True)
df.dropna(inplace=True)
# Binary encoding: Ransomware -> 1, Benign -> 0
df['Label'] = df['Label'].apply(lambda x: 1 if x == 'Ransomware' else 0)

4. Model Training with Balanced Classification

# RandomForest with balanced class weights for imbalanced data
model = RandomForestClassifier(
n_estimators=100,
random_state=42,
class_weight='balanced',  # Critical for rare event detection
n_jobs=-1
)
model.fit(X_train, y_train)

5. Security-Focused Evaluation

# Focus on recall for ransomware detection
print(classification_report(y_test, y_pred, target_names=['Benign', 'Ransomware']))
# Analyze false negatives (missed attacks) - most critical metric
cm = confusion_matrix(y_test, y_pred)
print(f"False Negatives (Missed Ransomware): {cm[1,0]}")

6. Feature Importance for Network Forensics

# Identify most predictive network features
importances = model.feature_importances_
top_features = X.columns[np.argsort(importances)[-15:]]

Success Criteria

- Recall for Ransomware Class: >95% (minimize missed attacks)

- Precision Balance: Maintain reasonable precision to avoid alert fatigue

- F1-Score: >0.85 for overall model effectiveness

- False Negative Rate: <5% (critical for security applications)

- Model Interpretability: Clear feature importance rankings for forensic analysis

- Processing Speed: Real-time inference capability for network monitoring

- Deploy model in network monitoring infrastructure

- Integrate with SIEM systems for automated alerting

- Provide forensic insights for incident response teams

Next Steps & Extensions

Immediate Improvements

- Multi-class Classification: Distinguish between different ransomware families

- Time Series Analysis: Incorporate temporal patterns in network flows

- Feature Selection: Apply dimensionality reduction for faster inference

Advanced Techniques

- Deep Learning: LSTM networks for sequential network pattern analysis

- Ensemble Methods: Combine with other anomaly detection approaches

- Transfer Learning: Adapt model to new ransomware variants with minimal retraining

Production Deployment

- Real-time Pipeline: Stream processing integration with network taps

- Model Monitoring: Drift detection and automated retraining workflows

- Alert Integration: SOAR platform connectivity for automated incident response

Domain Expansion

- Cross-Protocol Analysis: Extend to email, web, and DNS traffic patterns

- Threat Intelligence: Integrate with external threat feeds and IOCs

- Zero-Day Detection: Behavioral analysis for unknown ransomware variants

This project demonstrates practical application of machine learning for critical cybersecurity defense, providing both technical implementation skills and real-world security value for network protection.