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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Analýza malware na úrovni síťových toků
 Brázda, Šimon ; Setinský, Jiří (referee) ; Poliakov, Daniel (advisor)
This thesis explores freely available datasets and investigates their applicability to training machine learning models. The ipfixprobe tool was used to extract data from the dataset and the Python language was used for further implementation. In the theoretical part, basic application protocols, network monitoring capabilities at the flow level are discussed. Furthermore, different types of malware and types of machine learning models applicable to network flow classification were discussed. Subsequently, these models were used to test the applicability of the selected dataset, which was thus validated.
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Evaluation Of Flow-Induced Voltage Fluctuations Of Gas Chemiresistors By Parametric Empirical Model
 Mivalt, Filip
Analyses of voltage or current fluctuations in gas chemical sensors provide precise evaluation metrics for non-flowing gases. Automatic analysis of sensed flowing gas fluctuations is challenging task. The signal is a superposition of more stochastic processes. The presented paper proposes a machine learning empirical model for further automated parametrical analysis of voltage fluctuations produced by a gas sensor and flowing air.
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