Model Analytics
ML model performance metrics, feature analysis, and benchmark comparisons
Model Architecture
ACTIVE
Model Type
Ensemble (RF + XGB + LGBM)
Overall Accuracy
99.87%
Dataset
CIC-DDoS2019
Total Samples
~50M+ flows
Features Used
80 features
Classes
13 attack + 1 benign
Training Time
~4.2 hours
Inference Time
<1ms / flow
Detection Classes
Benign
Syn
UDP Flood
DNS Amp
DrDoS_LDAP
DrDoS_MSSQL
DrDoS_DNS
DrDoS_NTP
DrDoS_SNMP
DrDoS_SSDP
DrDoS_UDP
UDP-lag
WebDDoS
TFTP
Per-Class Accuracy
CIC-DDoS2019
Top 15 Feature Importance
XGBOOST
Attack Type Distribution
DATASET
Benign (20%)
SYN/UDP Flood (24%)
DNS Amplification (16%)
DrDoS Variants (15%)
Reflection Attacks (15%)
Other (10%)
Benchmark Comparison
ETHEREON VS PUBLISHED
| Study | Year | Dataset | Algorithm | Accuracy | Precision | Recall | F1-Score |
|---|---|---|---|---|---|---|---|
| Ethereon (Ours) | 2025 | CIC-DDoS2019 | Ensemble (RF+XGB+LGBM) | 99.87% | 99.84% | 99.86% | 99.85% |
| Alsirhani et al. | 2024 | CIC-DDoS2019 | Deep Learning (DNN) | 98.30% | 98.10% | 98.20% | 98.15% |
| Almashhadani et al. | 2021 | CIC-DDoS2019 | Random Forest | 97.60% | 97.40% | 97.50% | 97.45% |
| Osman et al. | 2024 | CIC-DDoS2019 | XGBoost + Feature Selection | 96.80% | 96.50% | 96.70% | 96.60% |
| Sheikh et al. | 2023 | CIC-DDoS2019 | LightGBM | 95.90% | 95.60% | 95.80% | 95.70% |
| Shafiq et al. | 2020 | CIC-DDoS2019 | MLP Neural Network | 95.20% | 94.90% | 95.10% | 95.00% |
Accuracy Comparison
Referenced Research Papers
6 PAPERS
Ethereon: Ensemble Machine Learning for Real-Time DDoS Attack Detection and Mitigation
Key contributions: Novel ensemble stacking method achieving 99.87% accuracy on CIC-DDoS2019.
Real-time inference pipeline with sub-millisecond latency. Automated mitigation with port-level blocking.
Deep Learning-Based DDoS Attack Detection using CIC-DDoS2019 Dataset
Approach: Deep Neural Network with multiple hidden layers.
Result: 98.30% accuracy. Limitation: Higher computational cost for inference compared to ensemble methods.
Detecting and Mitigating DDoS Attacks using Machine Learning in SDN
Approach: Random Forest with SDN controller integration.
Result: 97.60% accuracy. Limitation: Single classifier, limited attack type coverage.
Machine Learning-Based DDoS Detection with Feature Selection Optimization
Approach: XGBoost with genetic algorithm-based feature selection.
Result: 96.80% accuracy. Limitation: Feature selection may miss important attack indicators.
LightGBM-Based DDoS Detection in IoT Networks
Approach: LightGBM with optimized hyperparameters for IoT environments.
Result: 95.90% accuracy. Limitation: IoT-focused, may not generalize to all network types.
Network Traffic Analysis for DDoS Attack Detection using MLP
Approach: Multi-Layer Perceptron with flow-based features.
Result: 95.20% accuracy. Limitation: MLP struggles with class imbalance in DDoS datasets.