Engineering Commons^™

Automated Reverse Engineering Of Automotive Can Bus Controls, Charles Barron Kirby, Bryson Payne

KSU Proceedings on Cybersecurity Education, Research and Practice

This research provides a means of automating the process to reverse engineer an automobile’s CAN Bus to quickly recover CAN IDs and message values to control the various systems in a modern automobile. This approach involved the development of a Python script that uses several open-source tools to interact with the CAN Bus, and it takes advantage of several vulnerabilities associated with the CAN protocol. These vulnerabilities allow the script to conduct replay attacks against the CAN Bus and affect various systems in an automobile without the operator’s knowledge or interaction.

These replay attacks can be accomplished by capturing recorded …

Adaptive Randomized Rounding In The Big Parsimony Problem, Sangho Shim, Sunil Chopra, Eunseok Kim

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Forensic Analysis Of Spy Applications In Android Devices, Shinelle Hutchinson, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

Smartphones with Google's Android operating system are becoming more and more popular each year, and with this increased user base, comes increased opportunities to collect more of these users' private data. There have been several instances of malware being made available via the Google Play Store, which is one of the predominant means for users to download applications. One effective way of collecting users' private data is by using Android Spyware. In this paper, we conduct a forensic analysis of a malicious Android spyware application and present our findings. We also highlight what information the application accesses and what it …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher

Student Research Symposium

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

Development Of A Design Guideline For Pile Foundations Subjected To Liquefaction-Induced Lateral Spreading, Milad Souri, Arash Khosravifar

Student Research Symposium

Past earthquakes confirmed that seismically induced kinematic loads from soil lateral spreading and inertial loads from structure can cause severe damages to pile foundations. The research questions are:

• How to combine inertial and kinematic loads in design of pile foundations in liquefied soil?
• How the combination of inertia and kinematics changes with depth?
• How this combination is affected by long-duration earthquakes?
• How this combination affects inelastic demands in piles?

Low Cost Vehicular Autonomy Using Radar And Gps, Nathan Jessurun, Ryan Gordon, Danielle Fredette

The Research and Scholarship Symposium (2013-2019)

This presentation describes a subset of the systems devised for this year's autonomous golf cart senior design project. Our goal is to explore the possibilities of low cost autonomy using only radar and GPS for environmental sensing and navigation. Although autonomous and semi-autonomous ground vehicles are a relatively new reality, prototypes have been a subject of engineering research for decades, often utilizing an array of sensors and sensor fusion techniques. State of the art autonomous ground vehicle prototypes typically use a combination of LIDAR and other distance sensors (such as radar or sonar) as well as cameras and GPS, sometimes …

Quantifying Iron Overload Using Mri, Active Contours, And Convolutional Neural Networks, Andrea Sajewski, Stacey Levine

Undergraduate Research and Scholarship Symposium

Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, …

Astria Ontology: Open, Standards-Based, Data-Aggregated Representation Of Space Objects, Jennie Wolfgang, Kathleen Krysher, Michael Slovenski, Unmil P. Karadkar, Shiva Iyer, Moriba K. Jah

Space Traffic Management Conference

The necessity for standards-based ontologies for long-term sustainability of space operations and safety of increasing space flights has been well-established [6, 7]. Current ontologies, such as DARPA’s OrbitOutlook [5], are not publicly available, complicating efforts for their broad adoption. Most sensor data is siloed in proprietary databases [2] and provided only to authorized users, further complicating efforts to create a holistic view of resident space objects (RSOs) in order to enhance space situational awareness (SSA).

The ASTRIA project is developing an open data model with the goal of aggregating data about RSOs, parts, space weather, and governing policies in order …