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Možnosti identifikace botnetové robotické aktivitiy
Prajer, Richard ; Palovský, Radomír (vedoucí práce) ; Pavlíček, Luboš (oponent)
Tato diplomová práce zkoumá možné způsoby, jak odhalovat robotickou aktivitu botnetů na síti. Nejprve se věnuje jejich detekci založené na analýze úplných paketů, a to prostřednictvím DNS, HTTP a IRC komunikace. Detekci založenou na analýze úplných paketů však shledává z technických i etických důvodů neuplatnitelnou. Poté se zaměřuje na analýzu vycházející ze záznamů metadat o síťových tocích, které upravuje tak, aby byly zpracovatelné strojovým učením. Pomocí různých metod strojového učení pak vytváří detekční modely, jejichž úspěšnost poměřuje. Jako přijatelně úspěšná se pro odhalování robotické aktivity botnetů projevuje metoda bayesovských sítí. Model s její pomocí vytvořený ovšem odhaluje pouze botnety, které již plní úkoly zadávané C&C servery. "Spící" botnety tedy tento model spolehlivě detektovat nedokáže.
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Noise, Transport and Structural Properties of High Energy Radiation Detectors Based on CdTe
Šik, Ondřej ; Lazar, Josef (oponent) ; Navrátil, Vladislav (oponent) ; Grmela, Lubomír (vedoucí práce)
Because of demands from space research, healthcare and nuclear safety industry, gamma and X-ray imaging and detection is rapidly growing topic of research. CdTe and its alloy CdZnTe are materials that are suitable to detect high energy photons in range from 10 keV to 500 keV. Their 1.46 -1.6 eV band gap gives the possibility of high resistivity (10^10-10^11 cm) crystals production that is high enough for room temperature X-ray detection and imaging. CdTe/CdZnTe detectors under various states of their defectiveness. Investigation of detector grade crystals, crystals with lower resistivity and enhanced polarization, detectors with asymmetry of electrical characteristics and thermally degenerated crystals were subject of my work in terms of analysis of their current stability, additional noise, electric field distribution and structural properties. The results of the noise analysis showed that enhanced concentration of defects resulted into change from monotonous spectrum of 1/f noise to spectrum that showed significant effects of generation-recombination mechanisms. Next important feature of deteriorated quality of investigated samples was higher increase of the noise power spectral density than 2 with increasing applied voltage. Structural and chemical analyses showed diffusion of metal material and trace elements deeper to the crystal bulk. Part of this work is also focused on surface modification by argon ion beam and its effect on chemical and morphological properties of the surface.
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Exploitation of GPU in graphics and image processing algorithms
Jošth, Radovan ; Svoboda, David (oponent) ; Trajtel,, Ľudovít (oponent) ; Herout, Adam (vedoucí práce)
This thesis introduces several selected algorithms, which were primarily developed for CPUs, but based on high demand for improvements; we have decided to utilize it on behalf of GPGPU. This modification was at the same time goal of our research. The research itself was performed on CUDA enabled devices. The thesis is divided in accordance with three algorithm’s groups that have been researched: a real-time object detection, spectral image analysis and real-time line detection. The research on real-time object detection was performed by using LRD and LRP features. Research on spectral image analysis was performed by using PCA and NTF algorithms and for the needs of real-time line detection, we have modified accumulation scheme for the Hough transform in two different ways. Prior to explaining particular algorithms and performed research, GPU architecture together with GPGPU overview are provided in second chapter, right after an introduction. Chapter dedicated to research achievements focus on methodology used for the different algorithm modifications and authors’ assess to the research, as well as several products that have been developed during the research. The final part of the thesis concludes our research and provides more information about the research impact.
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Acceleration of Object Detection Using Classifiers
Juránek, Roman ; Kälviäinen, Heikki (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
Detection of objects in computer vision is a complex task. One of most popular and well explored approaches is the use of statistical classifiers and scanning windows. In this approach, classifiers learned by AdaBoost algorithm (or some modification) are often used as they achieve low error rates, high detection rates and they are suitable for detection in real-time applications. Object detection run-time which uses such classifiers can be implemented by various methods and properties of underlying architecture can be used for speed-up of the detection. For the purpose of acceleration, graphics hardware, multi-core architectures, SIMD or other means can be used. The detection is often implemented on programmable hardware. The contribution of this thesis is to introduce an optimization technique which enhances object detection performance with respect to an user defined cost function. The optimization balances computations of previously learned classifiers between two or more run-time implementations in order to minimize the cost function. The optimization method is verified on a basic example -- division of a classifier to a pre-processing unit implemented in FPGA, and a post-processing unit in standard PC.
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