840 research outputs found

    Sistema de ahorro energético con sensor inalámbrico de sueño

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    In the following paper, perform the development and evaluation of a device capable of identifying the sleep state of a person from the measurement of the respiratory frequency, integrating it into a Z-Wave system to perform spatial domotic control. It was evidenced, by means of the analysis of electrical consumption tests, how the implementation of the system generates a significant saving of 38% with respect to the average monthly consumption in a house, and how its application contributes to an environment conducive to sleep by reducing noise that generate the multimedia equipment in the room -off immediately after a detection-. The correct functioning of the sensor is observed due to the characteristics of the measurements of quality of service (QoS) parameters: Delay, Throughput and Jitter, guaranteeing that the loss of information is null and that the system implemented is viable.En el siguiente artículo se realiza el desarrollo y evaluación de un dispositivo capaz de identificar el estado de sueño de una persona a partir de la medición de la frecuencia respiratoria, integrándolo a un sistema tipo Z-Wave para realizar control domótico espacial. Se evidenció, por medio del análisis de pruebas de consumo eléctrico, cómo la implementación del sistema genera un ahorro significativo del 38% con respecto al consumo promedio mensual en una vivienda, y cómo su aplicación contribuye con un entorno propicio para dormir al disminuir el ruido que generan los equipos multimedia en la habitación -apagados inmediatamente después de una detección-. Se observa un correcto funcionamiento del sensor por las características de las mediciones de los parámetros de calidad de servicio (QoS): Delay, Throughput y Jitter, garantizándose que la pérdida de información es nula y que el sistema implementado es viable.&nbsp

    Detecção de anomalias na partilha de ficheiros em ambientes empresariais

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    File sharing is the activity of making archives (documents, videos, photos) available to other users. Enterprises use file sharing to make archives available to their employees or clients. The availability of these files can be done through an internal network, cloud service (external) or even Peer-to-Peer (P2P). Most of the time, the files within the file sharing service have sensitive information that cannot be disclosed. Equifax data breach attack exploited a zero-day attack that allowed arbitrary code execution, leading to a huge data breach as over 143 million user information was presumed compromised. Ransomware is a type of malware that encrypts computer data (documents, media, ...) making it inaccessible to the user, demanding a ransom for the decryption of the data. This type of malware has been a serious threat to enterprises. WannaCry and NotPetya are some examples of ransomware that had a huge impact on enterprises with big amounts of ransoms, for example WannaCry reached more than 142,361.51inransoms.Inthisdissertation,wepurposeasystemthatcandetectfilesharinganomalieslikeransomware(WannaCry,NotPetya)andtheft(Equifaxbreach),andalsotheirpropagation.Thesolutionconsistsofnetworkmonitoring,thecreationofcommunicationprofilesforeachuser/machine,ananalysisalgorithmusingmachinelearningandacountermeasuremechanismincaseananomalyisdetected.Partilhadeficheiroseˊaatividadededisponibilizarficheiros(documentos,vıˊdeos,fotos)autilizadores.Asempresasusamapartilhadeficheirosparadisponibilizarficheirosaosseusutilizadoresetrabalhadores.Adisponibilidadedestesficheirospodeserfeitaapartirdeumaredeinterna,servic\codenuvem(externo)ouateˊPontoaPonto.Normalmente,osficheiroscontidosnoservic\codepartilhadeficheirosconte^mdadosconfidenciaisquena~opodemserdivulgados.Oataquedeviolac\ca~odedadosrealizadoaEquifaxexplorouumavulnerabilidadedediazeroquepermitiuexecuc\ca~odecoˊdigoarbitraˊrio,levandoaqueainformac\ca~ode143milho~esdeutilizadoresfossecomprometida.Ransomwareeˊumtipodemalwarequecifraosdadosdocomputador(documentos,multimeˊdia...)tornandoosinacessıˊveisaoutilizador,exigindoaesteumresgateparadecifraressesdados.Estetipodemalwaretemsidoumagrandeameac\caaˋsempresasatuais.WannaCryeNotPetyasa~oalgunsexemplosdeRansomwarequetiveramumgrandeimpactocomgrandesquantiasderesgate,WannaCryalcanc\coumaisde142,361.51 in ransoms. In this dissertation, we purpose a system that can detect file sharing anomalies like ransomware (WannaCry, NotPetya) and theft (Equifax breach), and also their propagation. The solution consists of network monitoring, the creation of communication profiles for each user/machine, an analysis algorithm using machine learning and a countermeasure mechanism in case an anomaly is detected.Partilha de ficheiros é a atividade de disponibilizar ficheiros (documentos, vídeos, fotos) a utilizadores. As empresas usam a partilha de ficheiros para disponibilizar ficheiros aos seus utilizadores e trabalhadores. A disponibilidade destes ficheiros pode ser feita a partir de uma rede interna, serviço de nuvem (externo) ou até Ponto-a-Ponto. Normalmente, os ficheiros contidos no serviço de partilha de ficheiros contêm dados confidenciais que não podem ser divulgados. O ataque de violação de dados realizado a Equifax explorou uma vulnerabilidade de dia zero que permitiu execução de código arbitrário, levando a que a informação de 143 milhões de utilizadores fosse comprometida. Ransomware é um tipo de malware que cifra os dados do computador (documentos, multimédia...) tornando-os inacessíveis ao utilizador, exigindo a este um resgate para decifrar esses dados. Este tipo de malware tem sido uma grande ameaça às empresas atuais. WannaCry e NotPetya são alguns exemplos de Ransomware que tiveram um grande impacto com grandes quantias de resgate, WannaCry alcançou mais de 142,361.51 em resgates. Neste tabalho, propomos um sistema que consiga detectar anomalias na partilha de ficheiros, como o ransomware (WannaCry, NotPetya) e roubo de dados (violação de dados Equifax), bem como a sua propagação. A solução consiste na monitorização da rede da empresa, na criação de perfis para cada utilizador/máquina, num algoritmo de machine learning para análise dos dados e num mecanismo que bloqueie a máquina afetada no caso de se detectar uma anomalia.Mestrado em Engenharia de Computadores e Telemátic

    Machine learning for smart building applications: Review and taxonomy

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    © 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

    Hybrid CNN+LSTM Deep Learning Model for Intrusions Detection Over IoT Environment

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    The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning-based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a hybrid CNN+LSTM deep learning model for the detection of network intrusions. In this research, we detect DDOS types of network intrusions, i.e., R2L, R2R, Prob, and which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 99.82% as demonstrated in the experimental results

    Real-TV : RTP/L3\u27s visual monitor

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    Today many real-world activities are being monitored and controlled by some type of micro-processor based system. Emphasis is placed on manufacturing and controlling through the use of computers. The inherent nature of these real-world applications makes them difficult to control and monitor. They must be built meeting strict timing constraints and must be predictable. We need to develop simplistic, reliable, and cost effective methods for building, evaluating, monitoring and controlling these complex systems. These tools should provide the user with system insight. At the New Jersey Institute of Technology Real-Time Computing Laboratory, we are building a system that will provide the aforementioned tools in a complete real-time environment. This paper gives the user a brief overview of the system that is currently under construction and gives a detailed look into the component called Real-TV. Real-TV is a software component that will be used as a monitoring tool to provide users with system insight. Processes and other system statistics, occurring within the simulated predictable real-time software/hardware environment, can be tracked and monitored

    Energy Measurements of High Performance Computing Systems: From Instrumentation to Analysis

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    Energy efficiency is a major criterion for computing in general and High Performance Computing in particular. When optimizing for energy efficiency, it is essential to measure the underlying metric: energy consumption. To fully leverage energy measurements, their quality needs to be well-understood. To that end, this thesis provides a rigorous evaluation of various energy measurement techniques. I demonstrate how the deliberate selection of instrumentation points, sensors, and analog processing schemes can enhance the temporal and spatial resolution while preserving a well-known accuracy. Further, I evaluate a scalable energy measurement solution for production HPC systems and address its shortcomings. Such high-resolution and large-scale measurements present challenges regarding the management of large volumes of generated metric data. I address these challenges with a scalable infrastructure for collecting, storing, and analyzing metric data. With this infrastructure, I also introduce a novel persistent storage scheme for metric time series data, which allows efficient queries for aggregate timelines. To ensure that it satisfies the demanding requirements for scalable power measurements, I conduct an extensive performance evaluation and describe a productive deployment of the infrastructure. Finally, I describe different approaches and practical examples of analyses based on energy measurement data. In particular, I focus on the combination of energy measurements and application performance traces. However, interweaving fine-grained power recordings and application events requires accurately synchronized timestamps on both sides. To overcome this obstacle, I develop a resilient and automated technique for time synchronization, which utilizes crosscorrelation of a specifically influenced power measurement signal. Ultimately, this careful combination of sophisticated energy measurements and application performance traces yields a detailed insight into application and system energy efficiency at full-scale HPC systems and down to millisecond-range regions.:1 Introduction 2 Background and Related Work 2.1 Basic Concepts of Energy Measurements 2.1.1 Basics of Metrology 2.1.2 Measuring Voltage, Current, and Power 2.1.3 Measurement Signal Conditioning and Analog-to-Digital Conversion 2.2 Power Measurements for Computing Systems 2.2.1 Measuring Compute Nodes using External Power Meters 2.2.2 Custom Solutions for Measuring Compute Node Power 2.2.3 Measurement Solutions of System Integrators 2.2.4 CPU Energy Counters 2.2.5 Using Models to Determine Energy Consumption 2.3 Processing of Power Measurement Data 2.3.1 Time Series Databases 2.3.2 Data Center Monitoring Systems 2.4 Influences on the Energy Consumption of Computing Systems 2.4.1 Processor Power Consumption Breakdown 2.4.2 Energy-Efficient Hardware Configuration 2.5 HPC Performance and Energy Analysis 2.5.1 Performance Analysis Techniques 2.5.2 HPC Performance Analysis Tools 2.5.3 Combining Application and Power Measurements 2.6 Conclusion 3 Evaluating and Improving Energy Measurements 3.1 Description of the Systems Under Test 3.2 Instrumentation Points and Measurement Sensors 3.2.1 Analog Measurement at Voltage Regulators 3.2.2 Instrumentation with Hall Effect Transducers 3.2.3 Modular Instrumentation of DC Consumers 3.2.4 Optimal Wiring for Shunt-Based Measurements 3.2.5 Node-Level Instrumentation for HPC Systems 3.3 Analog Signal Conditioning and Analog-to-Digital Conversion 3.3.1 Signal Amplification 3.3.2 Analog Filtering and Analog-To-Digital Conversion 3.3.3 Integrated Solutions for High-Resolution Measurement 3.4 Accuracy Evaluation and Calibration 3.4.1 Synthetic Workloads for Evaluating Power Measurements 3.4.2 Improving and Evaluating the Accuracy of a Single-Node Measuring System 3.4.3 Absolute Accuracy Evaluation of a Many-Node Measuring System 3.5 Evaluating Temporal Granularity and Energy Correctness 3.5.1 Measurement Signal Bandwidth at Different Instrumentation Points 3.5.2 Retaining Energy Correctness During Digital Processing 3.6 Evaluating CPU Energy Counters 3.6.1 Energy Readouts with RAPL 3.6.2 Methodology 3.6.3 RAPL on Intel Sandy Bridge-EP 3.6.4 RAPL on Intel Haswell-EP and Skylake-SP 3.7 Conclusion 4 A Scalable Infrastructure for Processing Power Measurement Data 4.1 Requirements for Power Measurement Data Processing 4.2 Concepts and Implementation of Measurement Data Management 4.2.1 Message-Based Communication between Agents 4.2.2 Protocols 4.2.3 Application Programming Interfaces 4.2.4 Efficient Metric Time Series Storage and Retrieval 4.2.5 Hierarchical Timeline Aggregation 4.3 Performance Evaluation 4.3.1 Benchmark Hardware Specifications 4.3.2 Throughput in Symmetric Configuration with Replication 4.3.3 Throughput with Many Data Sources and Single Consumers 4.3.4 Temporary Storage in Message Queues 4.3.5 Persistent Metric Time Series Request Performance 4.3.6 Performance Comparison with Contemporary Time Series Storage Solutions 4.3.7 Practical Usage of MetricQ 4.4 Conclusion 5 Energy Efficiency Analysis 5.1 General Energy Efficiency Analysis Scenarios 5.1.1 Live Visualization of Power Measurements 5.1.2 Visualization of Long-Term Measurements 5.1.3 Integration in Application Performance Traces 5.1.4 Graphical Analysis of Application Power Traces 5.2 Correlating Power Measurements with Application Events 5.2.1 Challenges for Time Synchronization of Power Measurements 5.2.2 Reliable Automatic Time Synchronization with Correlation Sequences 5.2.3 Creating a Correlation Signal on a Power Measurement Channel 5.2.4 Processing the Correlation Signal and Measured Power Values 5.2.5 Common Oversampling of the Correlation Signals at Different Rates 5.2.6 Evaluation of Correlation and Time Synchronization 5.3 Use Cases for Application Power Traces 5.3.1 Analyzing Complex Power Anomalies 5.3.2 Quantifying C-State Transitions 5.3.3 Measuring the Dynamic Power Consumption of HPC Applications 5.4 Conclusion 6 Summary and Outloo

    Multimodal Biometric Analysis for Monitoring of Wellness

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    Biometric data can provide useful information about person's overall wellness. The focus of this dissertation is wellness monitoring and diagnostics based on behavioral and physiological traits. The research comprises of three studies: passive non-intrusive biometric monitoring, active monitoring using a wearable computer, and a diagnostics of early stages of Parkinson's disease. In the first study, a biometric analysis system for collecting voice and gait data from a target individual has been constructed. A central issue in that problem is filtering of data that is collected from non-target subjects. A novel approach to gait analysis using floor vibrations has been introduced. Naive Bayes model has been used for gait analysis, and the Gaussian Mixture Model has been implemented for voice analysis. It has been shown that the designed biometric system can provide sufficiently accurate data stream for health monitoring purposes.In the second study, a universal wellness monitoring algorithm based on a binary classification model has been developed. It has been tested on the data collected with a wearable body monitor SenseWear®PRO and with the Support Vector Machines acting as an underlying binary classification model. The obtained results demonstrate that the wellness score produced by the algorithm can successfully discriminate anomalous data.The focus of the final part of this thesis is an ongoing project, which aims to develop an automated tool for diagnostics of early stages of Parkinson's disease. A spectral measure of balance impairment is introduced, and it is shown that that measure can separate the patients with Parkinson's disease from control subjects
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