Graduate Research Assistant | Master’s by Research Faculty of Artificial Intelligence and Engineering Multimedia University, Malaysia
I am a systems-oriented cybersecurity researcher working at the intersection of:
- Quantum-safe networking
- QKD/PQC service management
- AI-driven cybersecurity
- Encrypted traffic intelligence
- Few-shot and zero-day threat detection
- Reproducible research software
My current research focuses on building software, simulation, and control frameworks for secure future digital infrastructure, especially QKD/PQC-enabled networks, O-RAN, 5G/6G, IoT, IoMT, and encrypted modern traffic.
I am preparing for PhD research in quantum network systems, quantum-safe service management, and AI-secure future networks, with particular interest in research groups such as QuTech / TU Delft.
I build reproducible software and control frameworks for quantum-safe and AI-secure future networks.
My work connects two major layers of secure future infrastructure:
| Research Layer | Main Contribution |
|---|---|
| Quantum-Safe Network Systems | QKD/PQC orchestration, key-pool control, crypto-agility, closed-loop simulation, service management |
| AI-Assisted Cyber Resilience | Encrypted traffic intelligence, few-shot adaptation, zero-day detection, drift recovery, domain-shift robustness |
|
Reproducible QKD/PQC network service-management simulator QKD Studio is a discrete-time simulator and testbed for studying service-management problems in quantum key distribution networks. Focus areas
Research value Supports deployable and controllable quantum-safe network infrastructure, including key-as-a-service, service-level management, and adaptive network control. |
Few-shot encrypted traffic classification under domain shift and zero-day conditions TrafficMAML is a reproducible AI cybersecurity framework for adaptive traffic classification when labelled data is limited and deployment domains change. Focus areas
Research value Studies how AI can support resilient network operations when protocols, traffic distributions, and threat classes evolve. |
Quantum-Safe Future Networks
│
├── QKD/PQC Service Management
│ ├── QKD key-pool dynamics
│ ├── Crypto-agile service control
│ ├── Routing and scheduling policies
│ └── Closed-loop controller evaluation
│
├── AI-Assisted Cyber Resilience
│ ├── Encrypted traffic classification
│ ├── Few-shot and zero-day detection
│ ├── Domain-shift robustness
│ └── Drift recovery
│
└── Reproducible Research Software
├── Deterministic experiments
├── Seed-level reporting
├── Benchmark design
└── Open research artifacts
| No. | Title | Venue / Status |
|---|---|---|
| 1 | Evading the Strategic Eavesdropper: Adversarial Bandits for Quantum-Safe O-RAN | IEEE WIFS, in review |
| 2 | QKD Studio: A Reproducible Discrete-Time Testbed for Service-Management and Closed-Loop Control Experiments in Quantum Key Distribution Networks | IEEE TNSM, in review |
| 3 | A Systematic Literature Review on Data-Efficient and Adaptive Learning Techniques for Encrypted Traffic Classification under Modern Protocols | MDPI Computers, published |
| 4 | Benchmarking Deep and Ensemble Learning for HTTPS Traffic Classification: The Case for Packet-Burst Statistics and Interpretability | IEEE TENSYMP, accepted |
| 5 | The Adaptation-Detection Tension in Few-Shot Traffic Classification: A Dual Readout for Zero-Day Detection | IEEE Access, ready to submit |
| 6 | Few-Shot Meta-Learning Under Domain Shift: A Reproducible Control-Led Study of IoT and IoMT Traffic Classification | Elsevier Computer Networks, ready to submit |
- Quantum key distribution networks
- Post-quantum cryptography
- Quantum internet systems
- QKD/PQC orchestration
- Crypto-agility
- Key-as-a-service
- Network service management
- O-RAN and 5G/6G security
- Encrypted traffic classification
- Few-shot and meta-learning
- Zero-day detection
- Domain adaptation
- Drift detection and recovery
- IoT and IoMT network security
- Trustworthy AI for network defence
- Research software engineering
- Network simulation
- Benchmark design
- Deterministic experiments
- Seed-level reporting
- Open-source research artifacts
| Area | Tools and Technologies |
|---|---|
| Programming | Python, Rust, SQL, Java, C++, C#, JavaScript, TypeScript |
| Machine Learning | PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, CatBoost |
| Data Science | NumPy, Pandas, Polars, Spark, BigQuery, DuckDB, PostgreSQL |
| Network Security | DPI/DSI, TLS, QUIC, VPN traffic, flow statistics, anomaly detection |
| Quantum-Safe Networking | QKD simulation, PQC/QKD orchestration, crypto-agility, key-as-a-service modelling |
| Software Systems | FastAPI, Flask, REST APIs, WebSocket APIs, Docker, Git, Linux |
| Research Tools | MLflow, DVC, LaTeX, reproducible pipelines, ablation studies |
- Gold Medal, ITEX 2026 for AI-driven quantum-safe innovation involving PQC and QKD
- Fully funded Graduate Research Assistantship at Multimedia University
- IEEE TENSYMP accepted paper
- MDPI Computers published article
- Research manuscripts currently under review at IEEE TNSM and IEEE WIFS
- Public research software in QKD/PQC network simulation and few-shot traffic classification
I am interested in PhD opportunities related to:
- Quantum network systems
- Quantum internet software and control stacks
- QKD/PQC service management
- AI-assisted cyber resilience
- Trustworthy digital infrastructure
- Encrypted traffic intelligence
- Secure O-RAN, 5G, and 6G systems
- Reproducible cybersecurity research software
My strongest fit is with research groups working on quantum network systems, secure future networks, network control, reproducible simulation, and AI-driven cybersecurity.
| Platform | Link |
|---|---|
| muntakim.cse@gmail.com | |
| GitHub | github.com/muntakim1 |
| linkedin.com/in/muntakim1 | |
| Website | muntakim.xyz |
| ORCID | 0009-0000-8368-6578 |
| Google Scholar | Muntakimur Rahaman |