Published on 22 Aug 2019

TECH TALK with Professor Ruby B. Lee

What would happen if business centres such as Wall Street go dark for a week?"

The question was posed to the audience at Nanyang Technopreneurship Centre's (NTC) first ever TECH TALK on 22 August 2019. Professor Ruby B. Lee made an appearance as the event's inaugural speaker, discussing the detection of anomalous behaviour in the power grid with deep learning.

Professor Lee is the Forest G Hamrick Professor in Engineering and Professor of Electrical Engineering at Princeton University. Her research revolves around three fields in particular - cyber security, computer architecture and deep learning - all of which were highlighted in her presentation.

During the session, she drew attention to security issues that the power grid system faces and the potential threat towards national security and economic activity. Given the power grid's role as the foundation for all computer systems and much of our daily lives, it is a prime target for attackers.

"But the focus on security critical infrastructure is not as well studied by the general research community", she said.

In response, she proposed deep learning solutions. Participants were given the opportunity to go through her findings and the thought process behind her research.

"Our vision is a bit broader than just protecting power grid systems," she said. "We are interested in asking the question - is there a simple way to just continuously monitor the security health of critical infrastructure?".

According to Professor Lee, deep learning models are more "accurate, precise and had better recall" than one-class machine learning models. Despite its appeal, they come with their own set of challenges. They have little attack data to draw on, complex processes and interdependencies to consider and they had to operate without domain-specific knowledge.

Professor Lee then presented her solution to the audience, a combination of the Temporal Deep Learning (Long Short Term Model) and Reconstruction Error Distribution. Instead of attack data, it utilises normal behaviour data for training. It compares it with run-time data and amplifies even the most subtle deviation from the norm, if any.

The model could also be applicable across cyber-physical systems. As they "generally do the same thing all the time", it allows for easier predictability of normal behaviour.

She wrapped up her talk by suggesting future improvements that can be made to this technology. Firstly, it should incorporate more normal and attack data for validity. Secondly, it should be taught to differentiate between various anomaly types. Thirdly, it should be generalised to other systems. Lastly, it should be able to update its reference normal data on a more frequent basis.

The session concluded with a quick Q&A with the attendees, and Professor Lee was then presented with a gift of appreciation. 

Aimed at raising awareness on cutting-edge technologies, TECH TALK is a series of 45-minute lectures by thought leaders and experts from academia. It serves to allow participants to explore new opportunities and build industry connections.

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