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.
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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Shukla, Diksha"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":["Vir V. Phoha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-24T07:00:00Z","date_published":"2019-08-24T07:00:00Z","updated_at":"2026-07-24T04:55:50Z","subjects":["Attacks","Authentication","Biometrics","Passwords","Security","Smart Devices","Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/etd/1060","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Vir V. Phoha"]},{"key":"dc:creator","label":"Author","values":["Shukla, Diksha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Attacks","Authentication","Biometrics","Passwords","Security","Smart Devices","Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/etd/1060"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
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.
"]},{"key":"dc:title","label":"Title","values":["Inferences from Interactions with Smart Devices: Security Leaks and Defenses"]}]}],"canonical_facts":{"dc:contributor":["Vir V. Phoha"],"dc:creator":["Shukla, Diksha"],"dc:description.abstract":["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.
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.
"],"dc:identifier":["https://surface.syr.edu/etd/1060"],"dc:subject":["Attacks","Authentication","Biometrics","Passwords","Security","Smart Devices","Engineering"],"dc:title":["Inferences from Interactions with Smart Devices: Security Leaks and Defenses"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:55:50Z"}