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Institute
Wie können mit Luftbildaufnahmen 3D Modelle generiert werden?
- Planen von kreisförmigen und einen rasterförmigen Flug Trajektorien.
- Autonomes Abfliegen und Aufnahme der Bilder
- Verortung der Bilder mittels GPS und Structure from Motion Algorithmen.
- Generierung von 3D Modellen mithilfe von Multi-View Stereo Algorithmen.
Durch Panoramen in Kombination mit dem ORB-SLAM ist ein schnelles Tracking möglich, liefert jedoch ausschließlich spärliche Daten. Durch die Kombination mit einem neuronalen Netz soll der SLAM Algorithmus zu einem RGBD-SLAM erweitert werden, um ein besseres Tracking und eine dichtere Punktwolke zu gewährleisten.
Virtual Machine Introspection (VMI) is a powerful technology used to detect and analyze malicious software inside Virtual Machines (VMs) from the outside. Asynchronous access to the VM’s memory can be insufficient for efficient monitoring of what is happening inside of a VM. Active VMI introduces breakpoints to intercept VM execution at relevant points. Especially for frequently visited breakpoints, and even more so for production systems, it is crucial to keep performance overhead as low as possible. In this paper, we present an empirical study that compares the performance of four VMI breakpoint-implementation variants—EPT switching (SLAT view switching) with and without fast single-stepping acceleration, instruction repair, and instruction emulation—from two VMI applications (DRAKVUF, SmartVMI) with the XEN hypervisor on 20 Intel Core i processors ranging from the fourth to the thirteenth generation. Instruction emulation was the fastest method across all 20 tested platforms. Modern processors such as the Intel Core i7 12700H and Intel Core i9 13900HX achieved median breakpoint-processing times as low as 15 µs for the emulation mechanism. The slowest method was instruction repair, followed by EPT switching and EPT switching with FSS. The order was the same for all measurements, indicating that this is a strong and generalizable result.
Function detection is a well-known problem in binary analysis. While prior work has focused on Linux/ELF, Windows/PE binaries have only partially been considered. This paper introduces FuncPEval, a dataset for Windows x86 and x64 PE files, featuring Chromium and the Conti ransomware, along with ground truth data for 1,092,820 function starts. Utilizing FuncPEval, we evaluate five heuristics-based (Ghidra, IDA, Nucleus, rev.ng, SMDA) and three machine-learning-based (DeepDi, RNN, XDA) function start detection tools. Among these, IDA achieves the highest F1-score (98.44%) for Chromium x64, while DeepDi closely follows (97%) but stands out as the fastest. Towards explainability, we examine the impact of padding between functions on the detection results, finding all tested tools, except rev.ng, are susceptible to randomized padding. The randomized padding significantly diminishes the effectiveness of the RNN, XDA, and Nucleus. Among the learning-based tools, DeepDi exhibits the least sensitivity, while Nucleus is the most adversely affected among the non-learning-based tools.
