I’m (as of November 2023) still in grad school at Iowa State University for Cybersecurity ME. Periodically, I write papers for these classes. I take the work seriously – they’re not publishable, but they are indicative of my thoughts and skills.
WireGuard: a software layer virtual private net- work you can use in the modern era. WireGuard is built into the Linux kernel and supports necessary extensible options with features like pre-shared keys, a configurable access control layer, and flexibility of functioning on top of virtual interfaces. Can we trust the network layer authentication (Layer 2) provided by the routing and handshake in WireGuard?
WireGuard: Trusting Network Layer Authentication in a New Protocol
Consumers feel the omnipresence of data sharing — one Amazon search turns to a deluge of ads on Facebook. A baby registry spawns fliers for minivans. Moving to a new home garners alarming calls about the perils of not having proper mortgage insurance. Is my phone listening to me? During the process of navigating online you are alerted that sites are tracking you (“Please accept these cookies to continue!”). These are raw facts collected about you, but analyzed information can be so much more, it has become a traded good. It exists as a nearly completely unregulated commodity. Categories with names like “Group O: Singles and Starters” contain subsets such as “O55 - Family Troopers: Families and single parent households living near military bases” shroud the concept that entire swaths of potential customers have been distilled to a homogeneous set of likely similar (and accurate) data points. The commodity marketplace already exists and it has an entire framework of regulations to draw on. Rather than inventing a complicated set of policies – one which would allow for exploitable loopholes! – the focus can be redirected at the process of exchanging data for money (or other data).
Detecting generated images is an increasingly preva- lent need in forensics. Convolutional neural networks (CNN) are a model of AI training that predates the more popular usage of transformers in popular language models today (e.g., GPT-1 and newer). I will continue to expand the application of CNN models to image assessment by training a model that contains two categories: real and synthetic human faces. This model will then be utilized to assess a variety of images generated from various methods (unknown to the trained model) of image generation. By asses a naive implementation (one without in-depth filtering or other image contexts), I am attempting to establish the feasibility of a long-term solution to more simple pre-trained models that can be used to AI generated image detection.
A Naive CNN Application to AI Generated Image Detection
See more in the supporting repository.