Visible to the public Biblio

Filters: Author is Kim, Kyu-Han  [Clear All Filters]
Conference Paper
Tung, Yu-Chih, Shin, Kang G., Kim, Kyu-Han.  2016.  Analog Man-in-the-middle Attack Against Link-based Packet Source Identification. Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing. :331–340.

A novel attack model is proposed against the existing wireless link-based source identification, which classifies packet sources according to the physical-layer link signatures. A link signature is believed to be a more reliable indicator than an IP or MAC address for identifying packet source, as it is generally harder to modify/forge. It is therefore expected to be a future authentication against impersonation and DoS attacks. However, if an attacker is equipped with the same capability/hardware as the authenticator to process physical-layer signals, a link signature can be easily manipulated by any nearby wireless device during the training phase. Based on this finding, we propose an attack model, called the analog man-in-the-middle (AMITM) attack, which utilizes the latest full-duplex relay technology to inject semi-controlled link signatures into authorized packets and reproduce the injected signature in the fabricated packets. Our experimental evaluation shows that with a proper parameter setting, 90% of fabricated packets are classified as those sent from an authorized transmitter. A countermeasure against this new attack is also proposed for the authenticator to inject link-signature noise by the same attack methodology.

Srivastava, Animesh, Jain, Puneet, Demetriou, Soteris, Cox, Landon P., Kim, Kyu-Han.  2017.  CamForensics: Understanding Visual Privacy Leaks in the Wild. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. :30:1–30:13.

Many mobile apps, including augmented-reality games, bar-code readers, and document scanners, digitize information from the physical world by applying computer-vision algorithms to live camera data. However, because camera permissions for existing mobile operating systems are coarse (i.e., an app may access a camera's entire view or none of it), users are vulnerable to visual privacy leaks. An app violates visual privacy if it extracts information from camera data in unexpected ways. For example, a user might be surprised to find that an augmented-reality makeup app extracts text from the camera's view in addition to detecting faces. This paper presents results from the first large-scale study of visual privacy leaks in the wild. We build CamForensics to identify the kind of information that apps extract from camera data. Our extensive user surveys determine what kind of information users expected an app to extract. Finally, our results show that camera apps frequently defy users' expectations based on their descriptions.