Understanding and Securing Typing Privacy in Wireless Environments

Yang, Edwin

Understanding and Securing Typing Privacy in Wireless Environments

dc.contributor.advisor	Fang, Song
dc.contributor.author	Yang, Edwin
dc.contributor.committeeMember	Cheng, Qi
dc.contributor.committeeMember	Hougen, Dean
dc.contributor.committeeMember	Kang, Ziho
dc.date.accessioned	2024-07-29T15:47:10Z
dc.date.available	2024-07-29T15:47:10Z
dc.date.issued	2024-08-01
dc.date.manuscript	2024-07-23
dc.description.abstract	Sensitive numbers play an unparalleled role in identification and authentication. Recent research has revealed plenty of side-channel attacks to infer keystrokes. The common idea is that pressing a key of a keyboard can cause a unique and subtle environmental change, which can be captured and analyzed by the eavesdropper to learn the keystrokes. However, these attacks also require either a training phase or a dictionary to build the relationship between an observed signal disturbance and a keystroke. As acquiring the training data about the victim is often unpractical, this research develops a side-channel attack that does not require training procedures. This dissertation demonstrates that typing a number creates not only a number of observed disturbances in space (each corresponding to a digit), but also a sequence of periods between each disturbance. Based upon existing work that utilizes inter-keystroke timing to infer keystrokes, we build a novel technique that combines the spatial and time domain information into a spatiotemporal feature of keystroke-disturbed wireless signals. With this spatiotemporal feature, the proposed attack can infer typed numbers without the aid of any training. Experimental results on top of software-defined radio platforms show that this attack vastly reduces the guesses required for breaking certain 6-digit PINs from 1 million to as low as 16, and can infer over 52% of user-chosen 6-digit PINs with less than 100 attempts. This dissertation also discusses feasible countermeasures that can resist the proposed attack and evaluates them in real-world typing environments.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/340524
dc.language	en_US	en_US
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Keystroke eavesdropping	en_US
dc.subject	SSN	en_US
dc.subject	PIN	en_US
dc.subject	Spatiotemporal correlation	en_US
dc.thesis.degree	Ph.D.	en_US
dc.title	Understanding and Securing Typing Privacy in Wireless Environments	en_US
ou.group	Gallogly College of Engineering::School of Computer Science	en_US
shareok.orcid	0000-0002-1794-5014	en_US