Speaker: Prof. Fabio Roli, Università di Cagliari

Machine learning algorithms are widely used for cybersecurity
applications, including spam, malware detection, biometric recognition.
In these applications, the learning algorithm has to face intelligent
and adaptive attackers who can carefully manipulate data to purposely
subvert the learning process. As machine learning algorithms have not
been originally designed under such premises, they have been shown to be
vulnerable to well-crafted attacks, including test-time evasion and
training-time poisoning attacks (also known as adversarial examples).
This talk aims to introduce the fundamentals of adversarial machine
learning and some techniques to assess the vulnerability of machine
learning algorithms to adversarial attacks. We report application
examples including object recognition in images, biometric identity
recognition, spam and malware detection.

Fabio Roli is a Full Professor of Computer Engineering at the University
of Cagliari, Italy, and Director of the Pattern Recognition and
Applications laboratory (http://pralab.diee.unica.it/). He is partner
and R&D manager of the company Pluribus One that he co-founded
(https://www.pluribus-one.it ). He has been doing research on the design
of pattern recognition and machine learning systems for thirty years.
His current h-index is 66 according to Google Scholar (December 2019).
He has been appointed Fellow of the IEEE and Fellow of the International
Association for Pattern Recognition. He was a member of NATO advisory
panel for Information and Communications Security, NATO Science for
Peace and Security (2008 – 2011).