Machine learning for IoT security

Current project

Additional projects can be negotiated with SEIT supervisors who work in a related field.

Click on this link to see a spreadsheet containing a list of supervisors in SEIT and their respective research areas. Please contact the supervisors directly to negotiate a project.

We also offer research projects for Masters by Research and Master of Philosophy degrees.

All admission enquires for SEIT research degree students (e.g. Phd, Masters, MPhil) can be directed to:



With the spread of IoT devices, security issues are becoming more severe, in part because of the large scale and heterogeneous nature of the devices.
There are an increasing number of insecure IoT devices with a high computational power, this makes them attractive targets for botnet creators.

Compromised IoT devices can be aggregated together through command and control servers to perform a diverse set of activities including; distributed denial of service, password cracking, and crypto-currency mining.

This project intends to present novel machine learning based approaches to address the above challenge, through the creation of new machine learning based techniques.

Research aims

  • A novel machine learning based process for the detection of and tracking of botnets.
  • A novel machine learning based process for the detection of malware activity on a local network.


Contact Dr. Tim Lynar for further information. Each potential student needs to write a research proposal highlighting research motivation, research problems, research objectives, brief review of the most relevant literature, proposed methodology and expected outcome.

Senior Lecturer
lensSchool of Engineering & Information Technology
lensUNSW Institute for Cyber Security
lensIntelligent Security
lensTrusted Autonomy