Sora Image Generator

Prompt: Create a professional flowchart diagram for a Hybrid Machine Learning Framework for Encrypted Malicious Traffic Detection. The diagram should follow a left-to-right or top-to-bottom pipeline with clearly labeled blocks and arrows. 🔹 Step 1: Input Data Block: UNSW-NB15 Dataset Description: Raw encrypted network traffic data ⬇️ 🔹 Step 2: Data Preprocessing Block: Data Preprocessing Include: Missing value handling Data cleaning Encoding (categorical → numerical) Normalization ⬇️ 🔹 Step 3: Feature Extraction (3 Parallel Channels) Split into three parallel branches: Static Features TLS/SSL features Certificate features Protocol metadata Dynamic Features Packet timing Flow duration Byte ratios Behavioral patterns Hybrid Features JA3/JA3S fingerprinting Metadata correlations Combined signatures ⬇️ (merge all three) 🔹 Step 4: Feature Processing Block: Feature Engineering & Optimization Include: Feature selection Correlation analysis SMOTE (data balancing) One-hot encoding Normalization ⬇️ 🔹 Step 5: Model Training (Parallel Models) Split into two branches: XGBoost Model (Supervised Learning) LightGBM Model (Semi-supervised Learning) ⬇️ 🔹 Step 6: Hybrid Ensemble (Stacking Layer) Block: Stacking Ensemble Model Combine predictions from both models Learn optimal combination ⬇️ 🔹 Step 7: Model Enhancement Block: Adversarial Training Synthetic Data Augmentation ⬇️ 🔹 Step 8: Output Block: Final Classification Benign Traffic Malicious Traffic ⬇️ 🔹 Step 9: Evaluation Metrics Accuracy Precision Recall F1 Score False Positive Rate 🎯 Design Tips Use different colors: Blue → Data stages Green → Feature engineering Orange → Models Purple → Ensemble Use arrows to show flow clearly Highlight parallel processing in feature extraction & models