Master of Science, The Ohio State University, 2024, Computer Science and Engineering

With the rise of car infotainment systems, the integration of smartphones with in-car displays has become increasingly prevalent. CarPlay, as one of the popular systems, is highly favored by users and is equipped in many vehicles. The Magic Brand Magic Box is an innovative Android-based device designed to interface with a car's CarPlay-enabled USB port, enabling the projection of its own user interface onto the car's display. However, this capability raises significant safety concerns, as it allows activities typically restricted while driving, such as watching videos on car screens. This thesis aims to reverse engineer the Magic Box to understand the mechanisms by which it communicates through the CarPlay interface. By analyzing the device's hardware and software, as well as referencing partial CarPlay protocol documents found online, we seek to uncover the principles behind its functionality and explore potential vulnerabilities in the Apple CarPlay system that may have been exploited. We aim to provide a detailed insight into the process of Android reverse engineering, offering valuable knowledge for researchers and practitioners interested in similar endeavors.

Committee: Keith Redmill (Advisor); Zhiqiang Lin (Advisor) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy, The Ohio State University, 2017, Industrial and Systems Engineering

This research explores deterministic and stochastic policies to help organizations make data-driven optimal decisions. The two major application areas identified in this research are manufacturing and cyber security. In a recent report published by McKinsey Analytics, the manufacturing industry uses only 20%-30% of the potential of data analytics. This suggests that there are still plenty of opportunities to use analytics in manufacturing processes. In the first part of my research, I formulate an Integer Programming model for the “stamping” process in automotive manufacturing. I develop a production scheduling method for automotive stamping to maintain optimal inventory positions. In stamping, different types of parts are scheduled for processing in the press, which requires different die-sets to be mounted on the press. This has all the elements of conventional scheduling problems with tardiness objectives and setup costs. Yet, it also has capacity constraints and part production constraints. We show that these constraints make solution with branch and bound difficult for problem sizes of interest. In this research, I use the structure of the scheduling problem and implemented heuristic methods like Genetic Algorithm alongside Earliest Due-date (EDD) rules to prioritize production of parts with low inventory as well as minimize the number of die-set changeovers. I call this new method Genetic Algorithm with Generalized Earliest Due-date (GAGEDD). I illustrate the computational advantages compared with alternatives and show its benefits using data from a real life automotive stamping press scheduling problem to build a decision support tool for the schedulers. The second part of this research is motivated towards improving cyber vulnerability maintenance policies under uncertainty. A conservative estimate by McAfee in 2014 puts annual cost of cybercrime at US$375B. This is an important contemporary issue where role of data analytics and optimization have a lot (open full item for complete abstract)

Committee: Theodore T. Allen PhD (Advisor); Cathy H. Xia PhD (Committee Member); Gagan Agrawal PhD (Committee Member) Subjects: Industrial Engineering; Operations Research