BREAKING SECURITY DEVICES

Project Summary: 

In recent years many physical attacks have been shown against IoT devices. Sensitive information processed by the circuitry in such devices can be leaked via physical characteristics of the device, such as power consumption, electromagnetic (EM) emanation, timing, etc. These techniques are known as Side-Channel Attacks (SCA).  To date, a significant amount of research has been carried out into side channel attacks, which uses statistical processing techniques to analyse the information leaked from the device.  Such attacks have been used to recover secret keys from remote keyless entry systems used by major car manufacturers, smart cards used in public transport ticketing applications, and encrypted bitstreams from FPGAs. In this proposed project, we aim to investigate the novel application of advanced machine learning techniques to improve the efficiency and practicality of side-channel analysis.

It is anticipated that this research will illustrate that advanced machine learning techniques can be used to perform successful side channel attacks on protected practical cryptographic implementations, which are currently considered to be resistant to such attacks. The goal is to target current real-world security devices so as to ensure research relevance.

Objectives 

The main objectives of the proposed research are:          

  • To study the state of the art in side channel analysis (SCA) attacks 
  • To investigate the application of machine learning to both software and hardware implementations and, in particular, to look at the impact of parameter selection and how different platforms affect profiling attacks.
  • To examine and compare various machine learning techniques on different platforms in order to determine in what scenarios the different learning techniques have an advantage.
  • To explore unsupervised machine learning techniques as we believe that they have considerable potential in the context of protected implementations.

Contact Details:

Supervisor Name: Máire O’Neill                                                                                

Tel: +44 (0)28 9097 1785

Email: m.oneill@ecit.qub.ac.uk