• Project
  • Publication
  • Resume

About Me

I received my Ph.D. from the Computer Science Engineering (CSE) department at the University of Michigan under the supervision of Prof. Kang G. Shin. During my Ph.D., my research interests lied primarily in the area of Vehicle Security. Recently, my interests shifted to Fraud and Identity; topics which I will work on at Lyft starting from Jun. 2018.


  • Jan.  2018    I will be joining    in Jun. 2018 as a Software Engineer on the Fraud and Identity team.
  • Aug. 2017    Our Viden paper got accepted to ACM CCS'17.
  • Jul.   2016    Our Bus-off Attack paper got accepted to ACM CCS'16.
  • May  2016    Our Clock-based IDS paper got accepted to USENIX Security'16.

Work Experience

  • Research Intern in Ford Silicon Valley Lab, Jun. 2017 - Aug. 2017
  • Research Intern in Intel Labs, May 2016 - Aug. 2016
  • Research Engineer at ETRI, Feb. 2010 - Mar. 2013

Professional Projects


  • Proposed a scheme which can identify who is driving the vehicle solely based on smartphone IMUs (i.e., gyroscope, accelerometer) and machine classifiers.
  • Achieved a driver identification accuracies of 95.3%, 95.4%, and 96.6% across 12, 8, and 5 drivers, respectively.
  • Viden09/2016

  • Proposed a scheme that can identify the attacker ECU with a low false identification rate of 0.2% and thus provide a pathway for forensic, isolation, and security patch.
  • Demonstrated in a CAN bus prototype and in two real vehicles via CAN data analysis.
  • CIDS12/2015

  • Proposed a new Intrusion Detection System which can fingerprint ECUs based on extracted clock skews and thus significantly outperforms state-of-the-art schemes.
  • Achieved a low false-positive rate of 0.055% in detecting intrusions thanks to the new fingerprinting scheme.
  • Bus-off Attack05/2015

  • Discovery of a new type of Denial-of-Service attack which can shut down ECUs or the whole in-vehicle network.
  • Demonstration of its feasibility and its severe consequences in two real vehicles.
  • VSense12/2014

  • Developed a mobile application which fuses smartphone IMUs/sensors to detect various types of driving patterns.
  • Achieved 100% and 97% accuracies in detecting left/right turns and lane changes, respectively.
  • Brake Anomaly Detection07/2014

  • Designed a new scheme which detects various abnormal brake operations (e.g., unintended acceleration).
  • Demonstrated its accurate detection via extensive CarSim simulation.
  • Selected Publications

    Honors and Awards

    • ACM CCS Student Travel Grant, 2016~2017
    • USENIX Security Student Travel Grant, 2016
    • Rackham Conference Travel Grant, 2015~2017
    • Distinguished Thesis Award, Seoul National University, 2010
    • Best Paper Award, JCCI, 2009
    • Graduate Magna Cum Laude with High Honors, Yonsei University, 2008
    • Academic Excellence Award with High Honors, Yonsei University, 2005