Power-Constrained Implantable Medical Device Security
Faculty Mentor: Dr. N. Sertac Artan
Objective: Evaluate low-power encryption and authentication methods for their suitability in secure implantable medical device implementations and develop metrics to compare their performance under the same setting.
Implementation: Students will gain practical research experience by implementing low-power encryption and authentication methods with hardware description languages and systematically comparing their performance under various use scenarios for implantable medical devices.
User Authentication Research for Mobile Devices
Faculty Mentor: Dr. Kiran Balagani
Objective: Explore the performance of authentication technologies in touch-based mobile devices under forgery and morphing attacks.
Implementation: Students will develop strategies and codes to inject attacks into state-of-the-art touch-based authentication algorithms and gain hands on research experience by designing and implementing adversarial attacks on these touch authentication algorithms.
Faculty Mentor: Dr. Ziqian (Cecilia) Dong
Objective: Investigate effective smartphone geolocation techniques in metropolitan areas when GPS does not function well due to lack of line of sight and interference.
Implementation: Students will help collecting network measurements (network delay, bandwidth, data transmission rate, received signal strength and geographical location) using applications on smartphones, in both cellular and wireless networks.
Privacy-Protected Automated Medical Data Collection on Smartphones
Faculty Mentor: Dr. Huanying (Helen) Gu
Objective: Raise the awareness of students to privacy and anonymity issues related to the collection and manipulation of medical data and design a framework that manipulates health-related information and provides strong privacy guarantees.
Implementation: Students will develop a tool to automatically extract and de-identify collected medical data. Furthermore, they will identify the critical privacy problems in the existing system, and architect a more privacy-friendly system for automated medical data collection.
Malicious mobile application (Malware) detection in smartphones
Faculty Mentor: Wenjia Li, Ph.D.
Objective: In this project, student(s) will get involved in the research efforts on how we can identify malicious mobile applications in an effective and efficient manner. Students will have the opportunity to develop mobile applications that can keep track of many attributes related to the Android system, such as the network traffic usage, battery usage, permission required, memory usage, etc. Based on all of these collected attribute data, some machine learning or data mining algorithms will be applied to distinguish benign (normal) mobile applications from malicious (unwanted) mobile applications.
Implementation: students are expected to develop mobile applications to collect and record the attributes of the Android phones, and then use machine learning/data mining techniques to analyze these attributes and build a model to distinguish malicious applications (malware) from normal ones.
Safety and Privacy in Robotic Telepresence
Faculty Mentor: Dr. Chung Hyuk Park
Objective: Develop a methodology for robotic system for tele-medicine that can enhance privacy in personal data and physical safety during interaction with a tele-operated robot.
Implementation: Students will develop and integrate different modules (i.e. depth-based visual perception, gesture recognition, and teleoperative robot control) that can run synchronously in a Linux system with a physical robot.
Usable Authentication Mechanisms for Android Devices
Faculty Mentor: Dr. Jonathan Voris
Objective: In this project, students will have an opportunity to study existing Android applications and get hands on experience developing new authentication techniques. Skills: Students participating in this project should be proficient in Java. Experience with Android or Linux development is helpful but not required.
Implementation: Project students will be responsible for analyzing applications from the Google play store in terms of their input requirements in order to assess their potential as authenticators. Next, they will implement a sensor for recording user interactions with such applications. Lastly, students will implement their own authentication technique and integrate it with the Android kernel.
Topology Control of Wireless Networks
Faculty Mentor: Dr. Tao Zhang
Objective: Study the tradeoffs between topology control performance metrics such as throughput and power drainage and network security and to investigate traffic patterns, network metrics when wireless networks are under botnet attack.
Implementation: Students will study tradeoffs between topology control performance metrics in wireless mesh networks and network security. They will design and build simulation test beds using open source software such as OMNET, measure and model power drainage and throughput deterioration due to botnet attacks.
Collective Model of Future Smartphone Botnets
Faculty Mentor: Dr. Xiaohui (Sean) Cui
Objective: Study the impact of smartphone botnets and find countermeasures by studying malware spreading on a botnet simulation platform.
Implementation: Students will contribute in creating adversarial smartphone-based malware and botnet software on smartphone emulation platforms, experimenting on the existing test bed, and collecting and analyzing experiment data. Students will also learn how to use botnet simulation tools, write script to collect experiment data and analyze data.
Implementation of Finite Field Arithmetic for Algebraic Cryptography on Mobile Devices
Faculty Mentor: Dr. Farshid Delgosha
Objective: Investigate efficient software implementation of arithmetic over small-size finite fields for implementing algebraic cryptosystems for mobile devices.
Implementation: Students will implement finite-field arithmetic cryptography for use on tablets.
Detection of Node Capture Attack in Smartphone-Backbone Sensing Cloud
Faculty Mentor: Dr. Wei Ding
Objective: Investigate methodologies to detect physical capture attack against smartphones.
Implementation: Students will help design a test phone, test circuits, and implement detection algorithms using Java or Python on smartphones.
School of Engineering and Computing Sciences