Physical side channel attacks, such as power analysis or electromagnetic radiation, are often used to extract secret information from computing devices. By monitoring these minute interactions between computation and the physical word, attackers have been able to extract secret keys from cryptographic implementations running on embedded devices such as smart cards and micro controllers.
Pivoting from embedded devices, in this talk we will discuss how to mount physical side channel attacks on laptop computers. Despite their complexity and speed, we will show that cryptographic implementation on PCs is also vulnerable to physical side channel attacks, which can be mounted cheaply and at large distances.
Next, we will also debunk the common belief that physical attacks require physical proximity and external measurement equipment. In particular, we will show that built-in microphones inadvertently capture electromagnetic side-channel leakage from ongoing computation. Moreover, this information is often conveyed by supposedly-benign channels such as audio recordings and common Voice-over-IP applications. Using this source of leakage, we show how attackers can obtain information about the victim’s browsing habits, as well as extract the machine's ECDSA signing keys.
Daniel Genkin is an Associate Professor at the School of Cybersecurity and Privacy at Georgia Tech. Daniel’s research interests are in hardware and system security, with particular focus on side channel attacks and defenses.
Daniel's work has won the Distinguished Paper Award at IEEE Security and Privacy 2019, an IEEE Micro Top Pick in 2019, the Black Hat Pwnie Award in 2014 and 2018, as well as top-3 paper awards in Crypto 2014 and CHES 2014 and 2016. Most recently, Daniel has been part of the team performing the first analysis of speculative and transient execution, resulting in the discovery of Spectre, Meltdown and followups. Daniel has a PhD in Computer Science from the Technion Israel's Institute of Technology.
Roei Schuster is a Postdoctoral Fellow at the Vector Institute for AI, hosted by prof. Nicolas Papernot. Roei is interested in the adversarial implications of emerging machine learning and natural-language processing technology. Prior to joining the Vector Institute, Roei completed his PhD in computer science at Tel Aviv University, with significant time spent as a researcher at Cornell Tech.