Polytechnic University of Turin

About: Polytechnic University of Turin is a education organization based out in Turin, Piemonte, Italy. It is known for research contribution in the topics: Finite element method & Nonlinear system. The organization has 11553 authors who have published 41395 publications receiving 789320 citations. The organization is also known as: POLITO & Politecnico di Torino.

Dual-Three Phase Induction Machine Drives Control-A Survey

Abstract: The paper aims to perform an overview on the state-of-the-art in the control of multi-phase drives employing dual-three phase induction machines. In particular, the paper is focused on modeling aspects, Pulse-Width Modulation (PWM) techniques for Voltage Source Inverters (VSI), Field Oriented Control (FOC) and Direct Torque Control (DTC) strategies for dual-three phase induction machines. Furthermore, the paper briefly presents the advantages of dual-three phase induction motor drives over the conventional three-phase drives and the different applications reported in the literature.

Reliable localization using set-valued nonlinear filters

Abstract: We propose a novel methodology for reliable localization of an autonomous mobile robot navigating in an unstructured environment using noisy absolute measurements from its exteroceptive sensors. A new deterministic filtering technique is introduced, which is based on the recursive computation of a bounding set that is guaranteed to contain the true state of the system, despite process and observation noise, and taking into explicit consideration uncertainties due to the linearization error. The proposed set-valued nonlinear filter relies on a two-step prediction-correction structure, with each step requiring the solution of a particular convex optimization problem. The method is illustrated by simulation on a localization problem for a nonholonomic rover, and it is compared with the standard extended Kalman filter approach.

Nazca: Detecting Malware Distribution in Large-Scale Networks

Abstract: Malware remains one of the most significant secu- rity threats on the Internet. Antivirus solutions and blacklists, the main weapons of defense against these attacks, have only been (partially) successful. One reason is that cyber-criminals take active steps to bypass defenses, for example, by distribut- ing constantly changing (obfuscated) variants of their malware programs, and by quickly churning through domains and IP addresses that are used for distributing exploit code and botnet commands. We analyze one of the core tasks that malware authors have to achieve to be successful: They must distribute and install malware programs onto as many victim machines as possible. A main vec- tor to accomplish this is through drive-by download attacks where victims are lured onto web pages that launch exploits against the users' web browsers and their components. Once an exploit is successful, the injected shellcode automatically downloads and launches the malware program. While a significant amount of previous work has focused on detecting the drive-by exploit step and the subsequent network traffic produced by malware programs, little attention has been paid to the intermediate step where the malware binary is downloaded. In this paper, we study how clients in real-world networks download and install malware, and present Nazca, a system that detects infections in large scale networks. Nazca does not operate on individual connections, nor looks at properties of the downloaded programs or the reputation of the servers hosting them. Instead, it looks at the telltale signs of the malicious network infrastructures that orchestrate these malware installa- tion that become apparent when looking at the collective traffic produced and becomes apparent when looking at the collective traffic produced by many users in a large network. Being content agnostic, Nazca does not suffer from coverage gaps in reputation databases (blacklists), and is not susceptible to code obfuscation. We have run Nazca on seven days of traffic from a large Internet Service Provider, where it has detected previously-unseen malware with very low false positive rates