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Machine Learning Based On-chip Detection of Security Attacks

  • Full or part time
  • Application Deadline
    Friday, April 03, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Supervisor: Basel Halak and Gopal Ramchurn

Project description

The proliferation of the internet of things technology has made it easier than ever for adversaries to have access to computing devices in order to extract data or inject malware to take control.

In addition, the multi-national distributed nature of the integrated circuit production business, has led to the emergence of new types of security threats at the hardware level such as hardware Trojans, wherein an attacker modifies the design to insert additional circuitry capable of carrying out malicious tasks such as leaking sensitive information or disabling the whole system.

The focus of the proposed project is to develop an on-chip monitor, which will use machine-learning algorithms to detect both cyber-attacks and hardware tampering. The design of such a monitor will rely on data derived from three main sources

1. The expected operation conditions (e.g. supply voltage and temperature)
2. The intrinsic characteristics of the underlying implementation technology (e.g. process variations)
3. The expected behaviour of existing hardware blocks in response to expected software activity, as measured by existing and novel performance and event monitoring hardware.
4.
The supervision team for the PhD is:
• Dr Basel Halak, an expert in digital design and hardware security
https://www.ecs.soton.ac.uk/people/bh1m10
• Professor Gopal Ramchurn, an expert in AI & Machine Learning https://www.ecs.soton.ac.uk/people/sdr1

The studentship is funded jointly by the UKRI MINDS CDT and ARM Holdings (https://www.arm.com). ARM will provide both industry expertise to support the research and offer internships at the company.

This project is funded through the UKRI MINDS Centre for Doctoral Training (www.mindscdt.ai). This is one of 16 PhD training centres in the UK with a unique focus on advancing AI techniques in the context of real-world engineered systems with a remit that spans novel hardware for AI, AI and machine learning, pervasive systems and IoT, and human-AI collaboration. We provide enhanced cross-disciplinary training in electronics and AI, entrepreneurship, responsible research and innovation, communication strategies, outreach and impact development as part of an integrated 4-year iPhD programme.

The MINDS CDT is based in a dedicated laboratory on Highfield Campus at the University of Southampton. The lab provides a supportive environment for individual research, ideas sharing and collaboration, and the wider campus provides access to substantial high-performance computing (including dedicated GPU servers), maker and cleanroom facilities. You will take part in our annual, student-designed innovation camps, be able to work with industry and government partners through our internship scheme and be able to take part in exchanges with international university partners.

Funding: full tuition for EU/UK Students plus, for UK and EU students resident in the UK for previous 3 years, an enhanced stipend of £18,285, tax-free per annum for 4 years. years.

Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 3 April 2020 for entry in October 2020.

How To Apply

Applications should be made online https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page . Please enter Safe, flexible and explainable reinforcement learning for autonomous systems under the Topic or Field of Research.

Applications should include:
Research Proposal
Curriculum Vitae
Two reference letters
Degree Transcripts to date

For further information please contact:

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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