Orientador: Mário Hissamitsu Tarumoto / Resumo: A observação de fenômenos naturais, como as mudanças de temperatura é bastante frequente no mundo atual, de forma que vários estudos têm sido realizados com o intuito de prever a ocorrência delas tendo em vista o que ocorreu no passado. Estudos desta natureza, em que a coleta de dados ocorre de forma contínua, seja por medida horária ou diária, não apresenta independência entre as observações. Entre as possíveis formas de análise, há a aplicação de técnicas de séries temporais ou também a teoria dos valores extremos. No entanto, um dos objetivos deste estudo é construir uma matriz de transição, de tal forma que possamos determinar a probabilidade, por exemplo, de alta temperatura amanhã, dado que hoje foi observado este fenômeno. Para a obtenção deste resultado, uma possibilidade é construir um modelo baseado em dados dependentes que seguem um processo de Markov, em que a suposição é de que exista dependência somente com o dia anterior. Neste trabalho, pretendemos construir este modelo e realizar a aplicação em dados de temperatura na cidade de Presidente Prudente-SP no período de janeiro de 2011 a dezembro de 2016. Posteriormente vamos realizar comparações entre o modelo markoviano de nido a partir da distribuição Weibull bivariada de Marshall e Olkin e outros modelos markovianos de nidos a partir das funções cópulas. / Abstract: The observation of natural phenomena, such as temperature changes, is quite frequent in the world today, so that several studies have been carried out with the intention of predicting their occurrence in view of what has happened in the past. Data of this nature, in which the data collection occurs continuously, whether by hourly or daily measurement, does not present independence between observations. Among the possible forms of analysis is the application of time-series techniques, however, the purpose of this study is to construct a transition matrix, so that we can determine the probability, for example, of high temperature tomorrow, since today this phenomenon was observed. To obtain this result, one possibility is to construct a model based on dependent data that follows a Markov process, in which the assumption is that there is dependence only with the previous day. In this work, we intend to build this model and perform the application on temperature data in the city of Presidente Prudente-SP from January 2011 to December 2016. For which comparisons were made between the Markovian model de ned from the distribution Weibull bivariate of Marshall and Olkin and other Markovian models de ned from the copula functions. / Mestre

When a network packet is formed by a computer's protocol stack, there are many components (e.g., Memory, CPU, etc.) of the computer that are involved in the process. The objective of this research is to identify, characterize and analyze the effects of the various components of a device (e.g., Memory, CPU, etc.) on the device's network traffic by measuring the changes in its network traffic with changes in its components. We also show how this characterization can be used to effectively perform counterfeit detection of devices which have counterfeit components (e.g., Memory, CPU, etc.). To obtain this characterization, we measure and apply statistical analyses like probability distribution fucntions (PDFs) on the interarrival times (IATs) of the device's network packets (e.g., ICMP, UDP, TCP, etc.). The device is then modified by changing just one component (e.g., Memory, CPU, etc.) at a time while holding the rest constant and acquiring the IATs again. This, over many such iterations provides an understanding of the effect of each component on the overall device IAT statistics. Such statistics are captured for devices (e.g., field-programmable gate arrays (FPGAs) and personal computers (PCs)) of different types. Some of these statistics remain stable across different IAT captures for the same device and differ for different devices (completely different devices or even the same device with its components changed). Hence, these statistical variations can be used to detect changes in a device's composition, which lends itself well to counterfeit detection. Counterfeit devices are abundant in today's world and cause billions of dollars of loss in revenue. Device components are substituted with inferior quality components or are replaced by lower capacity components. Armed with our understanding of the effects of various device components on the device's network traffic, we show how such substitutions or alterations of legitimate device components can be detected and hence perform effective counterfeit detection by statistically analyzing the deviation of the device's IATs from that of the original legitimate device. We perform such counterfeit detection experiments on various types of device configurations (e.g., PC with changed CPU, RAM, etc.) to prove the technique's efficacy. Since this technique is a fully network-based solution, it is also a non-destructive technique which can quickly, inexpensively and easily verify the device's legitimacy. This research also discusses the limitations of network-based counterfeit detection.

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