ACM 6th International Workshop on System and Network Telemetry and Analytics (SNTA'23)
In conjunction with HPDC 2023,
June 20, 2023, Orlando, Florida
Meeting Room: TBA
9am, June 20, 2023, EDT (USA)
6am, June 20, 2023, PDT (USA)
8am, June 20, 2023, CDT (USA)
9am, June 20, 2023, EDT (USA)
1pm, June 20, 2023, GMT
2pm, June 20, 2023 (England)
3pm, June 20, 2023 (Italy, France, Sweden, Switzerland)
9pm, June 20, 2023 (Taiwan)
10pm, June 20, 2023 (Korea)
Meeting ID: 957 3390 0673
* Zoom session is password protected, and only available during the session.
* All SNTA/HPDC registered participants will receive the zoom access information.
* This zoom session is only for SNTA workshop 2023.
* HPDC may have a separate zoom session info.
Thanks for your participation!
All video presentations will be available on YouTube SNTA Workshop channel, if available.
Registration: Please register using HPDC registration.
Zoom connection details will be sent to the list of registered attendees.
The Q&A session for papers is done through Slack (https://snta-workshop.slack.com) channel (#snta23).
This channel will remain open, and you can join the SNTA slack channel through this invitation link:
Program order subject to change.
SNTA 2023 Co-Chairs: Massimo Cafaro, Eric Chan-Tin, Jerry Chou, Jinon Kim
9:00am - 9:10am EDT
Chair: John Wu
9:10am - 10:00am EDT
Dr. David Mohaisen, University of Central Florida
Title: Understanding the Privacy Dimension of Wearables through Machine Learning-enabled Inferences
Abstract: To keep up with the ever-growing user expectations, developers keep adding new features to augment the use cases of wearables, such as fitness trackers, augmented reality head-mounted devices (AR HMDs), and smart watches, without considering their security and privacy impacts. In this talk, I will introduce our recent results on understanding the privacy dimension of wearables through inference attacks facilitated by machine learning and open research directions. First, I will present an exploration of the attack surface introduced by fitness trackers. We propose an inference attack that breaches location privacy through the elevation profiles collected by fitness trackers. Our attack highlights that adversaries can infer the location from elevation profiles collected via fitness trackers. Second, I will review the attack surface introduced by smartwatches by developing an inference attack that exploits the smartwatch microphone to capture the acoustic emanations of physical keyboards and successfully infers what the user has been typing. Third, I will present an exploration of the AR HMD’s through the design of an inference attack that exploits the geometric projection of hand movements in the air. The attack framework predicts the typed text on an in-air tapping keyboard, which is only visible to the user. I will conclude with lessons learned, defense directions, and open research directions.
Bio: David Mohaisen (PhD’12, University of Minnesota) is a Full Professor of Computer Science at the University of Central Florida, where he has been since 2017. Previously, he was an Assistant Professor at SUNY Buffalo (2015-2017) and a Senior Scientist at Verisign Labs (2012-2015). His research interests are in applied security and privacy, covering aspects of networked systems, software systems, IoT and AR/VR, machine learning, and blockchain systems. His research has been supported by several generous grants from NSF, NRF, AFRL, AFOSR, etc., and has been published in top conferences and journals, with multiple best paper awards. His work was featured in multiple outlets, including the New Scientist, MIT Technology Review, ACM Tech News, Science Daily, etc. Among other services, he is currently an Associate Editor of IEEE Transactions on Dependable and Secure Computing and served as an Associate Editor of IEEE Transactions on Mobile Computing (2 terms), IEEE Transactions on Parallel and Distributed Systems (1 term), and IEEE Transactions on Cloud Computing (1 term). He is a senior member of ACM (2018) and IEEE (2015), a Distinguished Speaker of the ACM, and a Distinguished Visitor of the IEEE Computer Society. Visit https://www.cs.ucf.edu/~mohaisen/ for more information.
10:00am - 10:15am EDT
Chair: John Wu
10:15am - 12:00pm EDT
Towards Securing UAV Flying Base Station: Misplacement Impact Analyses on Battery and Power
Sang-Yoon Chang, Kyungmin Park, Jonghyun Kim and Jinoh Kim
Insights into DoH: Traffic classification for DNS over HTTPS in an Encrypted Network
Fatema Bannat Wala, Scott Campbell and Mariam Kiran
Analyzing Transatlantic Network Traffic over Scientific Data Caches
Ziyue Deng, Alex Sim, Kesheng Wu, Chin Guok, Damian Hazen, Inder Monga, Fabio Andrijauskas, Frank Wurthwein and Derek Weitzel
Evaluating Unbalanced Network Data for Attack Detection
Donghwoon Kwon, Rares-Mihail Neagu, Prem Rasakonda, Jeong-Tak Ryu and Jinoh Kim