Machine learning for IoT security
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.
- 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.
Scholarships of $35,000 AUD for domestic and international students are available for Masters and PhD by research in fields that I am involved with. Please contact me for details.
Contact Dr. Tim Lynar (firstname.lastname@example.org) 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.