Uwe Muller, Eicke Hauck, Timm Welz, Jiska Classen, Matthias Hollick (Secure Mobile Networking Lab, TU Darmstadt)

Even though PowerPC mostly disappeared from the consumer device market, its architectural properties continue being popular for highly specialized systems. This particularly includes embedded systems with real-time requirements that are deeply integrated into critical infrastructures as well as aeronautics, transportation, control systems in power plants, etc. One example is Terrestrial Trunked Radio (TETRA), a digital radio system used in the public safety domain and deployed in more than 120 countries worldwide: base stations of at least one of the main vendors are based on PowerPC. Despite the criticality of the aforementioned systems, many follow a security by obscurity approach and there are no openly available analysis tools. While analyzing a TETRA base station, we design and develop a set of analysis tools centered around a PowerPC binary patcher. We further create various dynamic tooling on top, including a fast memory dumper, function tracer, flexible patching capabilities at runtime, and a fuzzer. We describe the genesis of these tools and detail the binary patcher, which is general in nature and not limited to our base station under test.

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Data Analytics and Expert Judgment in Time of Crisis:...

Igor Linkov, PhD Senior Science and Technology Manager, US Army Engineer Research and Development Center; Senior Data Analyst (on detail), FEMA/HHS R1 COVID Task Force; Adjunct Professor, Carnegie Mellon University

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Cross-National Study on Phishing Resilience

Shakthidhar Reddy Gopavaram (Indiana University), Jayati Dev (Indiana University), Marthie Grobler (CSIRO’s Data61), DongInn Kim (Indiana University), Sanchari Das (University of Denver), L. Jean Camp (Indiana University)

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Detecting Kernel Memory Leaks in Specialized Modules with Ownership...

Navid Emamdoost (University of Minnesota), Qiushi Wu (University of Minnesota), Kangjie Lu (University of Minnesota), Stephen McCamant (University of Minnesota)

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DRAGON: Predicting Decompiled Variable Data Types with Learned Confidence...

Caleb Stewart, Rhonda Gaede, Jeffrey Kulick (University of Alabama in Huntsville)

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