ML Model Training

Active Training Jobs
4
Running
Processing Time: 171s
Average Accuracy
83.61%
+2.8%
Across all models
Total Models
7
Deployed
In production
GPU Utilization
85%
High Load
Current usage
Training Process
1
Data Collection
Gather relevant datasets
2
Preprocessing
Clean and transform data
3
Feature Engineering
Select/create effective features
4
Model Training
Train and optimize model
5
Evaluation
Assess performance metrics
Current Phase Details
Feature Engineering
Selected Features

24 Optimal

Feature Score

0.85 High

Active Tasks
Feature Selection
In Progress
Correlation Analysis
Pending
Feature Importance
Pending
Training Progress

Real-time model training progress showing accuracy improvements. Current epoch showing 92.5% accuracy with 0.015 loss rate. Significant improvement from baseline of 78% accuracy.

Model Performance

Comparison of model performance metrics across different architectures. Quantum-enhanced model shows 35% improvement in threat detection and 28% reduction in false positives.

Resource Utilization

Current resource allocation and utilization metrics. GPU utilization at 85%, Memory usage at 72%, Network bandwidth at 45% of capacity.

Model Architecture Comparison

Comparative analysis of different model architectures. Hybrid quantum-classical approach shows optimal performance across all key metrics.

Training Timeline
Phase 1
Data Preparation

Dataset cleaning and preprocessing complete

Phase 2
Initial Training

Base model training and validation

Phase 3
Optimization

Fine-tuning and performance optimization

Literature Review & Comparative Analysis
Download as PDF Export Citations
Research Paper Approach Dataset Size Performance Key Findings Implementation Status
Quantum Federated Learning for Cybersecurity Nature Quantum Computing, Mar 2025 Hybrid QML 2.5M samples
98.5%
35% improvement in threat detection accuracy Implemented
Privacy-Preserving QML for Network Security IEEE Quantum, Feb 2025 Pure Quantum 1.8M samples
96.3%
28% reduction in false positives In Progress
Quantum-Classical Hybrid Defense Systems Science Advances, Mar 2025 Hybrid Defense 3.2M samples
97.8%
42% faster threat response time Implemented
Zero-Day Attack Detection with QML Quantum Information Processing, Feb 2025 Pure QML 1.5M samples
95.2%
65% improvement in zero-day detection In Progress
Quantum Federated Learning Framework

Distributed QML training across multiple nodes while preserving data privacy

Active Nodes
8 Connected
Global Model Version
v2.3.5
Methodology Comparison
Methodology Advantages Limitations Use Cases
Pure Quantum
  • • Maximum quantum advantage
  • • Superior for complex patterns
  • • Hardware requirements
  • • Cost intensive
 High-security environments
Hybrid QML
  • • Balance of performance/cost
  • • Easier implementation
  • • Complex integration
  • • Moderate overhead
 Enterprise cybersecurity
Training Logs
Model Name Type Status Progress Actions
Quantum-Enhanced CNN
Threat detection model
Hybrid Training
LSTM Network
Anomaly detection
Classical Training
Random Forest
Access pattern analysis
Classical Queued
XGBoost
Intrusion detection
Classical Queued