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Altay Ozen

dblp:199/2183 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Security and privacy · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Cyber-physical and IoT security · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cyber-physical and IoT security › robot security
robotic vehicle security
0.412020
From Control Model to Program: Investigating Robotic Aerial Vehicle Accidents with MAYDAY · USENIX Security Symposium 2020

Methods — techniques the papers use, named apart from their topics

control model analysis · 0.4
YearPublicationVenuePosition
2020 From Control Model to Program: Investigating Robotic Aerial Vehicle Accidents with MAYDAY
Taegyu Kim, Altay Ozen, Fei Fan 0002, Zhan Tu, Xiangyu Zhang 0001, Jing (Dave) Tian, Dongyan Xu
USENIX Security Symposium3
2017 PIANO: Proximity-Based User Authentication on Voice-Powered Internet-of-Things Devices
abstract
Voice is envisioned to be a popular way for humans to interact with Internet-of-Things (IoT) devices. We propose a proximity-based user authentication method (called PIANO) for access control on such voice-powered IoT devices. PIANO leverages the built-in speaker, microphone, and Bluetooth that voice-powered IoT devices often already have. Specifically, we assume that a user carries a personal voice-powered device (e.g., smartphone, smartwatch, or smartglass), which serves as the user's identity. When another voice-powered IoT device of the user requires authentication, PIANO estimates the distance between the two devices by playing and detecting certain acoustic signals; PIANO grants access if the estimated distance is no larger than a user-selected threshold. We implemented a proof-of-concept prototype of PIANO. Through theoretical and empirical evaluations, we find that PIANO is secure, reliable, personalizable, and efficient.
Neil Zhenqiang Gong, Altay Ozen, Richard Shin, Dawn Song, Hongxia Jin, Xuan Bao
ICDCS2