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Syracuse University

Inferences from Interactions with Smart Devices: Security Leaks and Defenses

Abstract

dc:description.abstract

<p>We unlock our smart devices such as smartphone several times every day using a pin, password, or graphical pattern if the device is secured by one. The scope and usage of smart devices' are expanding day by day in our everyday life and hence the need to make them more secure. In the near future, we may need to authenticate ourselves on emerging smart devices such as electronic doors, exercise equipment, power tools, medical devices, and smart TV remote control. While recent research focuses on developing new behavior-based methods to authenticate these smart devices, pin and password still remain primary methods to authenticate a user on a device. </p> <p>Although the recent research exposes the observation-based vulnerabilities, the popular belief is that the direct observation attacks can be thwarted by simple methods that obscure the attacker's view of the input console (or screen). In this dissertation, we study the users' hand movement pattern while they type on their smart devices. The study concentrates on the following two factors; (1) finding security leaks from the observed hand movement patterns (we showcase that the user's hand movement on its own reveals the user's sensitive information) and (2) developing methods to build lightweight, easy to use, and more secure authentication system. The users' hand movement patterns were captured through video camcorder and inbuilt motion sensors such as gyroscope and accelerometer in the user's device.</p>

Degree

thesis:*
Name thesis:degree_name
Doctor of Philosophy (PhD)
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Electrical Engineering and Computer Science
Year
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Shukla, Diksha
Contributors dc:contributor
  • Vir V. Phoha

Subjects

dc:subject × 7

Identifiers

dc:identifier.*
Repository record dc:identifier
https://surface.syr.edu/etd/1060
OAI identifier oai:identifier
oai:surface.syr.edu:etd-2061

Chain of custody

source
Harvested from
Syracuse University
Base URL
surface.syr.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Shukla, Diksha. Inferences from Interactions with Smart Devices: Security Leaks and Defenses. Dissertation thesis, 2019. https://surface.syr.edu/etd/1060