Advanced Research Extensions (Distant Stretch Goals) #31
Description
Advanced Research Extensions (Distant Stretch Goals)
Overview
This issue tracks ambitious, long-term research goals beyond core modernization. These are NOT required for the modernization, but would make excellent research contributions.
Timeline: 6+ months (after core modernization complete)
Priority: VERY LOW (distant future)
1. Multi-Dataset Generalization Study
Goal: Validate models across multiple IoT botnet datasets
Additional Datasets
- IoT-23 (2020) - 23 devices, modern attacks (~20GB)
- Edge-IIoTset (2022) - DDoS, ransomware, injection (~50GB)
- TON-IoT (2021) - Comprehensive testbed (~100GB+)
- CICIoT2023 (2023) - 105 devices, 33 attacks (~200GB)
Research Questions
- Cross-dataset generalization
- Transfer learning effectiveness
- Temporal generalization (2018 → 2023 attacks)
Research Value: HIGH - publication-worthy
2. PyTorch Implementation Comparison
Goal: Reimplement in PyTorch, compare frameworks
Deliverables
- PyTorch autoencoder/classifier
- PyTorch FL with Flower
- Framework comparison study
- Performance benchmarks
Learning Value: HIGH - master both frameworks
3. Advanced Federated Learning
Goal: Explore cutting-edge FL techniques
Techniques
- Personalized FL (per-device models)
- Differential Privacy
- Secure Aggregation
- Communication Efficiency (compression)
- Byzantine Robustness
Research Value: VERY HIGH - novel research
4. Modern Architectures
Goal: Test transformers, GNNs, self-supervised learning
Architectures
- Transformers for time-series
- Graph Neural Networks
- Self-supervised learning
- Few-shot learning
Research Value: HIGH - modern ML for IoT
5. Real-World Deployment
Goal: Production deployment study
Scenarios
- Edge deployment (TFLite on Raspberry Pi)
- Real-time detection (Kafka streaming)
- Production monitoring
- Adversarial robustness
Research Value: MEDIUM - practical impact
6. Comprehensive Benchmark Suite
Goal: Standardized benchmark for community
Components
- Multiple datasets
- Multiple models (classical + DL + FL)
- Standardized metrics
- Public leaderboard
Research Value: VERY HIGH - community impact
Prioritization
Highest Research Value:
- Advanced FL Research
- Comprehensive Benchmark
- Multi-Dataset Generalization
Highest Learning Value:
- PyTorch Comparison
- Modern Architectures
- Real-World Deployment
Estimated Effort
| Goal | Time | Difficulty | Research Value |
|---|---|---|---|
| Multi-Dataset | 3-4 months | Medium | High |
| PyTorch | 2-3 months | Low | Medium |
| Advanced FL | 6-12 months | Very High | Very High |
| Modern Architectures | 4-6 months | High | High |
| Deployment | 3-4 months | Medium | Medium |
| Benchmark | 6-9 months | High | Very High |
Total if doing all: 2-3 years (PhD scope)
Notes
- NOT required for core modernization
- Pick ONE or TWO max
- Publication/PhD-level research
- Complete core modernization first
Recommendation: Choose based on career goals (research vs industry)