Primary supervisor

Sanoop Mallissery

This project is not just another IDS project. It sits at the intersection of four powerful areas:

So you will deal with:

Cybersecurity: detecting attacks in IoT and IIoT networks.
Federated Learning: training AI without centralizing raw data.
Privacy Engineering: measuring and reducing leakage from model updates.
Edge AI: making the system lightweight enough for constrained devices.

Most Master’s projects stop at “my model achieved high accuracy.” This project goes beyond that by asking:

Can the model protect privacy?
Can it survive malicious clients?
Can it work under non-IID real-world IoT conditions?
Can it run efficiently at the edge?
Can we prove the privacy-security-efficiency trade-off experimentally?

Aim/outline

We aim to build a privacy-preserving Federated Learning-based Intrusion Detection System for IoT/IIoT networks. Instead of sending raw network traffic to a central server, multiple IoT clients collaboratively train an AI model while keeping their data local.

The project will explore one key question:

Can IoT devices learn together to detect cyberattacks without exposing private data?

So you will be developing a prototype that includes:

  1. IoT/IIoT dataset preparation using public IDS datasets.
  2. Non-IID client simulation to represent realistic IoT environments.
  3. Baseline IDS model development using ML/deep learning.
  4. Federated Learning pipeline using methods such as FedAvg/FedProx.
  5. Privacy protection using Differential Privacy or Secure Aggregation-inspired mechanisms.
  6. Optional robustness testing against poisoning/backdoor clients.

• 7. Optional edge-efficiency analysis using pruning, quantization, or communication reduction.

URLs/references

Datasets
Edge-IIoTset: https://ieee-dataport.org/documents/edge-iiotset-new-comprehensive-realistic-cyber-security-dataset-iot-and-iiot-applications
ToN-IoT: https://research.unsw.edu.au/projects/toniot-datasets
Bot-IoT: https://research.unsw.edu.au/projects/bot-iot-dataset
WUSTL-IIOT-2021: https://www.cse.wustl.edu/~jain/iiot2/index.html
CIC IIoT Dataset 2025: https://www.unb.ca/cic/datasets/iiot-dataset-2025.html

Federated Learning
FedAvg: https://proceedings.mlr.press/v54/mcmahan17a.html
FedProx: https://arxiv.org/abs/1812.06127
Federated Learning Survey: https://arxiv.org/abs/1912.04977

Privacy / Security
Differential Privacy Book: https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
Secure Aggregation: https://dl.acm.org/doi/10.1145/3133956.3133982
Deep Leakage from Gradients: https://arxiv.org/abs/1906.08935

Required knowledge

Essential:
Python, machine learning basics, cybersecurity fundamentals, network traffic/IDS concepts, data preprocessing, evaluation metrics such as accuracy, precision, recall, F1-score, and AUC.

Useful:
PyTorch or TensorFlow, Scikit-learn, Pandas, NumPy, Federated Learning frameworks such as Flower/FedML, basic Differential Privacy, Git/GitHub, Linux command line.

Nice to have:
Adversarial machine learning, poisoning/backdoor attacks, Secure Aggregation, model pruning/quantization, non-IID data partitioning, Docker, experiment tracking.