| 日時(Date) |
May 25th, 2026 3rd period (1:30-–3:00) |
|---|---|
| 場所(Location) | L2 |
| 司会(Chair) | Yuichi Hayashi |
| 講演者(Presenter) | Prof. Matteo Sereno, the University of Turin |
| 題目(Title) | Stochastic Models for Remote Timing Attacks |
| 概要(Abstract) | In this talk, we present the first remote timing attack based on formal stochastic models. Our attack uses queuing models from the field of performance evaluation to estimate the service times of different classes of network requests. By using Bayesian statistics, we then identify opportunities for remote timing attacks by answering the following inverse question: what is the probability that a given network request belongs to a target class, given an estimate of its service time? Our experimental evaluation on popular web applications and websites shows that our investigation is not just a theoretical exercise, because our attack outperforms existing empirical approaches in terms of standard performance figures. We believe that the formal foundations put forward in this talk can be successfully applied to the creation of principled remote timing attacks which are more effective, as they are better equipped to deal with the complexity of the problem. |
| 講演言語(Language) | English |
| 講演者紹介(Introduction of Lecturer) |
Matteo Sereno is a Full Professor at the University of Turin, Department of Computer Science. He received his MSc degree in Computer Science from the University of Salerno and his PhD in Computer Science from the University of Turin, supervised by Prof. Gianfranco Balbo. His research activity focuses on the performance evaluation of complex systems, including communication networks, distributed systems, and computing systems. He develops efficient analytical methods for modeling and analyzing such systems. In particular, his research interests include: a. modeling and evaluation of complex systems such as distributed applications, communication networks, and high-performance computing systems; b. reliability of critical systems; c. formalisms for performance evaluation and efficient algorithms; d. computational epidemiology. |