State-of-the-Art AI Analysis | 9 Datasets | 100M+ Records | 99.74% Accuracy
UNSW-NB15: Industry-standard network intrusion benchmark dataset
257,673 network connection records with 43 features
Balance of 10 categories: Normal traffic + 9 attack types
XGBoost achieves 90% accuracy in binary classification
Normal vs Attack detection with 98.5% ROC-AUC
Detailed classification across all 10 traffic types
Key indicators for intrusion detection
XGBoost vs Random Forest vs LightGBM
Detection performance varies by attack type
Precision, Recall, and F1-Score for each attack type
Distribution of protocols and services in traffic
Network behavior patterns across attack types
Bytes transferred and connection duration patterns
Summary of all key findings and model performance
3 Industry-Standard Benchmark Datasets Combined
UNSW-NB15 + CICIDS2017 + NSL-KDD = 3M+ records
Comparing attack distributions across UNSW-NB15 and NSL-KDD
Cutting-edge methods from 2024 research
Supervised, Unsupervised, and Explainability techniques
Top 8 features visualized in radar format
Accuracy, Precision, Recall, F1, and ROC-AUC
6 major insights from the comprehensive analysis
Multi-source analysis with all key metrics and findings
Neural Networks trained on NVIDIA RTX 3060 - outperforming traditional ML
1D-CNN (93.69%) beats XGBoost (90.04%) by 3.65%
CNN, DNN, and Autoencoder performance
8 models ranked by accuracy
1D-CNN, DNN, and Autoencoder layer designs
4 Datasets | 87M+ Records | 40+ Attack Types
UNSW-NB15 + CICIDS2017 + NSL-KDD + CIC IoT 2023
Deep FFN leads at 93.63% accuracy
40+ unique attack categories
Key findings from cutting-edge analysis
All key metrics and findings in one view
Implementing 2024-2025 State-of-the-Art Techniques
XGBoost achieves 94.25% with Chi-Square + SMOTE
2024-2025 papers achieving 99%+ accuracy
All 10+ models ranked by accuracy
NF-UNSW-NB15-V2 + RL Controller + SMOTE + Threshold Optimization | Only 0.06% from SOTA 99.8%
