Hocheol Shin

Personal Data

 Name:  Hocheol Shin 
Address:  291 Daehak-ro, Yuseong, Daejeon, Republic of Korea 
Email:  h...@kaist.ac.kr 

Work Experience

 Current PhD student in Electrical Engineering at System Security Laboratory, the School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
 

Research Interest

  • Sensor attacks and defenses
  • Cyber-physical & safety-critical systems (e.g. drones, autonomous cars, and medical devices) security
  • IoT Security
  • Wireless signal analysis & reversing

Publications

Aug 2016  Sampling Race: Bypassing Timing-based Analog Active Sensor Spoofing Detection on Analog-digital Systems
Hocheol Shin, Yunmok Son, Youngseok Park, Yujin Kwon, and Yongdae Kim.
10th USENIX Workshop on Offensive Technologies (USENIX WOOT 2016).
 
Aug 2016  This ain’t your dose: Sensor Spoofing Attack on Medical Infusion Pump
Youngseok Park, Yunmok Son, Hocheol Shin, Dohyun Kim, and Yongdae Kim.
10th USENIX Workshop on Offensive Technologies (USENIX WOOT 2016).
 
Jul 2016  Dissecting Customized Protocols: Automatic Analysis for Customized Protocols based on IEEE 802.15.4
Kibum Choi, Yunmok Son, Juhwan Noh, Hocheol Shin, Jaeyeong Choi and Yongdae Kim.
9th ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec 2016).
Best Paper Award
 
Aug 2015  Security Analysis of FHSS-type Drone Controller
Hocheol Shin, Kibum Choi, Youngseok Park, Jaeyeong Choi, and Yongdae Kim.
The 16th International Workshop on Information Security Applications (WISA 2015).
 
Aug 2015  Rocking Drones with Intentional Sound Noise on Gyroscopic Sensors
Yunmok Son, Hocheol Shin, Dongkwan Kim, Youngseok Park, Juhwan Noh, Kibum Choi, Jungwoo Choi, and Yongdae Kim.
Proceedings of the 24th USENIX Conference on Security Symposium (USENIX Security 2015).

Research Project Experiences

Jan 2017
~
Current
Development of Information-leakage Prevention and Drone ID Management Technologies for Safe Drone Services
Participation: Explored the possibility of sensor attacks against drone-mounted sensors: e.g. lidar, ultrasonic sensors, and vision sensors.
 
Jul 2016
~
Current
Security Analysis of Core Sensing Modules for Smart Vehicles
Participation: Overall project management and administration. Sensor spoofing & blinding attacks against one of Velodyne’s vehicular lidar models. Successfully induced multiple fake points at a controllable location by spoofing, and incapacitated the victim lidar from sensing an obejct by blinding. Invisible blinding attacks against one of Mobileye’s vehicular vision sensor. Succeeded in DoSing the victim vision sensor remotely with laser whose wavelength is invisible to human eye. Practical mitigative measures against discovered attacks were also studied and devised.
 
Oct 2014
~
Current
Development of Gateway for Safe IoT Networking
Participation: Optical channel sensor spoofing attack inducing over-infusion of medical fluids against the drop sensor of a off-the-shelf medical infusion pump.
 
Mar 2014
~
Feb 2015
Safe & Reliable Vehicular Environment Perception System for Personal Digital Electric Vehicles
Participation: Survey on the possibility of sensor spoofing attacks against vehicular radar modules. Showed by simulation that attackers can easily access and affect the victim radar’s measurement by “phase-code brute force”. In the simulation the victim radar was assumed to use Phase-Coded Modulation scheme, which is quite effective to suppressing accidental inter-radar interferences.
 
Mar 2014
~
Dec 2014
Safe Sensing & Networking for Smart Mobile Health-care
Participation: Sensor spoofing attack inducing fake heartbeat against optical heartbeat sensor on Samsung smart watch.
 
Aug 2013
~
Dec 2013
EEWS Smart Grid Project Planning
Participation: Survey on intrusion detection/protection systems in smart grid networks. Survey on possibility of hardware tempering and sensor attacks against smart meters’ electricity usage measurement subsystems.

Skills

 Hardware: Soldering, USRP, Photoreceiver circuit implementation (Basic), Voltage comparator circuit implementation (Basic)
 Frameworks: GNU Radio, Linux, Gephi (Basic), Tensorflow (Basic), Scikit-learn (Basic)
 Languages: C, C++, Python, Matlab, LaTeX