La Sapienza - Università di Roma

Speaker: Dorjan Hitaj.

Affiliation: Sapienza Università di Roma. 

Summary: With the boom of machine learning in the past decade, its use to improve the performance of cybersecurity systems became obvious. Cybersecurity systems employing machine learning techniques can analyze potential threat patterns, reason about past events, and adapt to evolving information. More importantly, machine learning models can aid cybersecurity analysts by suggesting various mitigation strategies, resulting in a rapid and swift response. At the same time, a growing body of work has demonstrated that machine learning models are susceptible to attacks compromising both the security and privacy guarantees of these models. While there does exist a tremendous amount of work proposing defense mechanisms, the concern regarding the robustness and reliability of machine learning solutions, especially in critical cybersecurity systems, is still present.

Biography: Dorjan is an Assistant Professor at Sapienza University of Rome. Previously he was a postdoctoral researcher at the same university and has been involved in multiple European and national research projects. His area of research is machine learning and cybersecurity where he has published numerous papers in various domains such as malware detection & mitigation, covert communication, digital forensics, password security and more. He is currently exploring the influence of machine learning, particularly deep learning, within the cybersecurity domain focusing on discovering weaknesses in machine learning techniques applied to cybersecurity and uncovering novel security threats stemming from this novel technology.

Registration: This seminar is restricted to the students of the Master's Degree in Cybersecurity.