Vyas Sekar
Professor, Electrical and Computer Engineering Carnegie Mellon University
- Pittsburgh PA
Vyas Sekar seeks to develop more rigorous foundations for securing tomorrow’s electric energy grid
Biography
Sekar received his Ph.D. from the computer science department at Carnegie Mellon University in 2010. He earned his bachelor's degree from the Indian Institute of Technology Madras, where he was awarded the President of India Gold Medal. His work has been recognized with best paper awards at ACM SIGCOMM, ACM CoNext, and ACM Multimedia.
Areas of Expertise
Media Appearances
This is AI's brain on AI
Axios online
2024-07-27
The bottom line: AI-generated data is "an amazingly useful technology, but if you use it indiscriminately, it's going to run into problems," Vyas Sekar, a professor of electrical and computer engineering at Carnegie Mellon University, told Axios.
7 ways to protect your iPhone from being hacked
Business Insider online
2022-08-19
Vyas Sekar, a professor of electrical and computer engineering at Carnegie Mellon University, says staying safe is all about "good digital hygiene."
Q&A with Vyas Sekar on the COVID-19 pandemic's impact on cybersecurity
Tech Xplore online
2020-04-06
Vyas Sekar, a professor in Carnegie Mellon's Electrical and Computer Engineering Department whose work focuses on network security, thinks that enterprises need to be thinking very critically about the security of their networks—maybe more now than ever.
Stop! Don’t Charge Your Phone This Way
The New York Times online
2019-11-18
Like scammers who steal debit card numbers by putting illegal card-reading devices, or skimmers, on A.T.M.s, hackers can easily rip out USB ports and replace them with their own malicious hardware, said Vyas Sekar, a professor at CyLab, a security and privacy research institute at Carnegie Mellon University.
Are Your Facebook Messages Really Private? Here’s the Truth
Time Magazine online
2017-09-20
Here, Facebook shares information on how some of its security works, while Vyas Sekar, a faculty member of CyLab at Carnegie Melon University and a professor of Electrical and Computer Engineering, breaks down how someone might still find ways around it.
Media
Social
Industry Expertise
Accomplishments
SIGCOMM Rising Star Award
2016
NSA Science of Security Award
2016
President of India Gold Medal
2003
Education
Carnegie Mellon University
Ph.D.
Computer Science
2010
Indian Institute of Technology, Madras
BTech
Computer Science
2003
Patents
Timeline framework for time-state analytics
US11943123
Determining a time-state metric includes receiving a stream of raw data values of an attribute. Each received raw data value of the attribute is associated with a timestamp. It further includes converting the received stream of raw data values into a timeline representation of the attribute over time. The timeline representation comprises a sequence of spans. A span comprises a span start time, a span end time, and a span value. The span value comprises an encoding of one or more values of the attribute over a time interval determined by the span start time and the span end time. It further includes determining a time-state metric according to a timeline request configuration. The timeline request configuration comprises one or more timeline operations. The time-state metric is computed at least in part by performing a timeline operation on the timeline representation of the attribute.
Reconfigurable wireless data center network using free-space optics
US10924183
A reconfigurable free-space optical inter-rack network includes a plurality of server racks, each including at least one switch mounted on a top thereof, where each top-mounted switch includes a plurality of free-space-optic link connector, each with a free-space optical connection to a free-space-optic link connector on another top-mounted switch, a single ceiling mirror above the plurality of server racks that substantially covers the plurality of server racks, wherein the single ceiling mirror redirects optical connections between pairs of free-space-optic link connectors to provide a clear lines-of-sight between each pair of connected free-space-optic link connectors, and a controller that preconfigures a free-space optical network connecting the plurality of server racks by establishing connections between pairs of free-space-optic link connectors, and that reconfigures connections between pairs of free-space-optic link connectors in response to network traffic demands and events.
Articles
CANdid: A Stealthy Stepping-stone Attack to Bypass Authentication on ECUs
Journal on Autonomous Transportation Systems2024
A high-entropy source of randomness is an essential component in any secure protocol, required to ensure that protocol elements, such as cryptographic keys, nonces, or salts, are unpredictable for the attackers. Resource-constrained embedded devices, such as Electronic Control Units (ECUs) in modern vehicles, often utilize weak sources of randomness due to the unavailability of true sources of randomness. In this article, we illustrate the ability of a relatively simple adversary to exploit this weakness within ECUs of vehicles produced by major manufacturers.
Why spectral normalization stabilizes gans: Analysis and improvements
Advances in Neural Information Processing Systems2021
Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs). However, current understanding of SN's efficacy is limited. In this work, we show that SN controls two important failure modes of GAN training: exploding and vanishing gradients. Our proofs illustrate a (perhaps unintentional) connection with the successful LeCun initialization. This connection helps to explain why the most popular implementation of SN for GANs requires no hyper-parameter tuning, whereas stricter implementations of SN have poor empirical performance out-of-the-box. Unlike LeCun initialization which only controls gradient vanishing at the beginning of training, SN preserves this property throughout training. Building on this theoretical understanding, we propose a new spectral normalization technique: Bidirectional Scaled Spectral Normalization (BSSN), which incorporates insights from later improvements to LeCun initialization: Xavier initialization and Kaiming initialization
