Real-time Logging Research Articles

Cloud migration and microservices optimization framework for large-scale enterprises

As large-scale enterprises increasingly adopt cloud computing, the need for a structured framework to guide seamless cloud migration and optimize microservices architecture becomes critical. This review presents a comprehensive designed to address the challenges and opportunities associated with transitioning to cloud environments and optimizing microservices for performance, scalability, and security. The review begins with an assessment of the enterprise’s current infrastructure, emphasizing a phased approach to cloud migration. It outlines key strategies such as lift-and-shift, re-platforming, and re-architecting, each tailored to specific organizational needs. The selection of the appropriate cloud model (public, private, or hybrid) and the use of multi-cloud strategies are discussed to ensure flexibility and risk mitigation during migration. For microservices optimization, focuses on designing services that are loosely coupled and highly scalable. Key elements include the use of service discovery, load balancing, API gateways, and database management strategies to improve the efficiency and reliability of microservices. Additionally, this introduces security best practices, including identity and access management (IAM), encryption protocols, and microservices-specific security measures such as Zero Trust Architecture. The review also highlights optimization strategies involving containerization (Docker) and orchestration (Kubernetes), alongside the integration of Continuous Integration/Continuous Delivery (CI/CD) pipelines for streamlined development and deployment. Performance monitoring, real-time logging, and proactive maintenance are emphasized for sustained efficiency and cost control in cloud environments. Case studies demonstrate the successful implementation of cloud migration and microservices optimization in large enterprises, showcasing both the benefits and common challenges. The review concludes by exploring future trends, such as serverless computing and AI-driven optimization, that will continue to shape cloud migration and microservices architecture.

Design and implementation of real-time log analysis based on ELK

With the rapid development of the IT industry, the integrated solution of real-time log analysis has become a member of the new generation of IT operation and maintenance structure. The traditional manual log analysis system is no longer suitable for the rapid development of the operation and maintenance system. The log analysis software commonly used in enterprises includes Elasticsearch, Logstash, and Kibana systems. The advantage is that it is free and open source. "Design and implementation of real-time log analysis based on ELK" was born for system maintenance and developers to understand server software and hardware information through logs, and check the cumbersome operations in the configuration process. Because frequent analysis of the log can understand the load of the server, performance security, so as to take timely measures to correct errors. If you manage dozens or hundreds of servers, there is no centralized log management system to collect and summarize the logs on all servers, which causes great difficulties for operation and maintenance managers. "Design and implementation of real-time log analysis based on ELK" uses Elasticsearch, Logstash, and Kibana to achieve unified collection and classification of server logs, which can solve the defects of traditional log analysis platforms, improve the efficiency of server application positioning problems, and help enterprises solve data center problems running problems of related applications.

Evaluating SIEM RADAR: A New Metric for Enhancing Regulatory and Compliance Efficiency

This research paper explores the modern cybersecurity landscape, particularly focusing on the risks associated with SIEM products and SOC services. It underscores the critical issue of insufficient logging practices that compromise an organization's threat detection and response capabilities, thereby increasing the risk of security breaches. The importance of real-time log retention to address evolving digital threats is highlighted, with recommended retention periods from authoritative sources such as the White House, OWASP, MITRE, and SANS. The paper also addresses scalability challenges due to the exponential growth of log data, the necessity for effective correlation within SIEM systems for timely threat detection, and the importance of compliance with various standards and regulations to enhance security. This comprehensive analysis provides valuable insights for cybersecurity professionals, organizations, and policymakers.

DLLog: An Online Log Parsing Approach for Large-Scale System

Syslog is a critical data source for analyzing system problems. Converting unstructured log entries into structured log data is necessary for effective log analysis. However, existing log parsing methods demonstrate promising accuracy on limited datasets, but their generalizability and precision are uncertain when applied to diverse log data. Enhancements in these areas are necessary. This paper proposes an online log parsing method called DLLog, which is based on deep learning and has the longest common subsequence. DLLog utilizes the GRU neural network to mine template words and applies the longest common subsequence to parse log entries in real-time. In the offline stage, DLLog combines multiple log features to accurately extract the template words, creating a log template set to assist online log parsing. In the online stage, DLLog parses log entries by calculating the matching degree between the real-time log entry and the log template in the log template set. This method also supports the incremental update of the log template set to handle new log entries generated by systems. We summarized the previous works and validated DLLog using real log data collected from 16 systems. The results demonstrate that DLLog achieves high parsing accuracy, universality, and adaptability.

Real-Time Diagnosis of Configuration Errors for Software of AI Server Infrastructure

Artificial intelligence (AI) server infrastructure has been built to support AI applications and handle data-intensive workloads. AI server infrastructure is the essential building blocks, and errors in AI server infrastructure may lead to fatal consequences to any AI applications built upon it. Compared to traditional software, software for AI server infrastructure is more configurable, and thus more likely to have configuration errors that might prevent correct software behaviors. Previous work on misconfiguration diagnosis requires sufficient execution history or manual intervention, and can hardly diagnose potential misconfigurations which are not triggered at launching. In this paper, we propose a real-time method to address these issues. Specifically, we combine program analysis and real-time log parsing to diagnose configuration errors. It maps each configuration option to the log code by applying program slicing only once, and parses real-time logs during the operation of the AI server without manual intervention. We evaluate the effectiveness of our approach on the core components of Hadoop, an exemplar AI Server Infrastructure Software. The results show that our method mapped more than 80% of the configuration options to log outputs, identified 90% of the configuration read sites as the slicing seeds, and successfully diagnosed about 10% configuration errors that can not be addressed by previous studies.

Development of a Low-cost IoT-based Remote Sensing Device for Real-time Atmospheric Data Acquisition and Weather Monitoring

The knowledge of basic meteorological parameters is needful in terms of understanding the atmospheric behavior for a given period and consequently also for creating the weather forecast. In this study, an IOT based atmospheric weather data acquisition device is designed to ease and improve on the current measuring technique of acquiring Temperature, Pressure, and Relative Humidity measurement at different altitude levels. This design aims at solving the problem of inadequate atmospheric data by monitoring atmospheric weather conditions using sensors while the microcontroller processes the data collected and relays it to the user. This research was carried out at the University of Uyo, between September 2021 and January, 2023. Taking cognizance of the fact that weather forecasting is of great relevance in our world, this system has been designed using a BME280 module for the atmospheric parameters capturing, an ESP8266 as the microcontroller for Data processing, and a wireless module for processing and transfer of the data from the BME module, a NEO6M GPS module for longitude and latitude, a Li-ion cell to power the components and then a TP4056 circuit to recharge the Li-ion cell. A web server application known as Thingspeak.com was incorporated to help the user interact and access the acquired data to make for ease of understanding and real-time logging of the data obtained. The device successfully transmitted real-time weather data of the target atmospheric parameters captured by it to its users remotely. The results prove that this device is optimally effective and viable for capturing atmospheric parameters for real-time monitoring purposes. This work is relevant in the weather forecasting sector, agricultural sector, as well as to individuals who may have need of gathering information about the atmosphere for proactive planning of daily activities.

Real-Time Gamma Ray Log Generation from Drilling Parameters of Offset Wells Using Physics-Informed Machine Learning

Summary By 2026, USD 5.05 billion will be spent per year on logging while drilling (LWD) according to the market report from Fortune Business Insights (2020). Logging tools and wireline tools are costly services for operators to pay for, and the companies providing the services also have a high cost of service delivery. They are, however, an essential service for drilling wells efficiently. The ability to computationally generate logs in real time using known relationships between the rock formations and drilling parameters, namely, rate of penetration (ROP), rotations per minute (RPM), surface weight on bit (SWOB), surface torque (TQX), standpipe pressure (SPPA), and hookload (HKLD), provides an alternative method to generate formation evaluation information (analysis of the subsurface formation characteristics such as lithology, porosity, permeability, and saturation). This paper describes an approach to creating a digital formation evaluation log generator using a novel physics-informed machine learning (PIML) approach that combines physics-based approaches with machine learning (ML) algorithms. The designed approach consists of blocks that calculate mechanical specific energy (MSE), physical estimates of gamma ray (GR) using physical and empirical models, and formation information. All this information and the drilling parameters are used to build a classification model to predict the formations, followed by formation-based regression models to get the final estimate of GR log. The designed PIML approach learns the relationships between drilling parameters and the GR logs using the data from the offset wells. The decomposition of the model into multiple stages enables the model to learn the relationship between drilling parameters data and formation evaluation data. It makes it easier for the model to generate GR measurements consistent with the rock formations of the subject well being drilled. Because the computationally generated GR by the model is not just dependent on the relationships between drilling parameters and GR logs, this model is also generalizable and capable of being deployed into the application with only retraining on the offset wells and no change in the model structure or complexity. For this paper, the drilling of the horizontal section will not be discussed, as this was done as a separate body of work. Historically collected data from the US Land Permian Basin wells are used as the primary data set for this work. Results from the experiments based on the data collected from five different wells have been presented. Leave-one-out validation for each of the wells was performed. In the leave-one-out validation process, four of the wells represent the set of offset wells and the remaining one becomes the subject well. The same process is repeated for each of the wells as they are in turn defined as a subject well. Results show that the framework can infer and generate logs such as GR logs in real time. The average root-mean-squared error (RMSE) observed from the experiments is 27.25 api, representing about 10% error. This error value is calculated based on the mean estimate and does not consider the predicted confidence interval. Considering the confidence interval helps further reduce the error margin.

An Unsupervised Gradient-Based Approach for Real-Time Log Analysis From Distributed Systems

We consider the problem of real-time log anomaly detection for distributed system with deep neural networks by unsupervised learning. There are two challenges in this problem, including detection accuracy and analysis efficacy. To tackle these two challenges, we propose GLAD, a simple yet effective approach mining for anomalies in distributed systems. To ensure detection accuracy, we exploit the gradient features in a well-calibrated deep neural network and analyze anomalous pattern within log files. To improve the analysis efficacy, we further integrate one-class support vector machine (SVM) into anomalous analysis, which significantly reduces the cost of anomaly decision boundary delineation. This effective integration successfully solves both accuracy and efficacy in real-time log anomaly detection. Also, since anomalous analysis is based upon unsupervised learning, it significantly reduces the extra data labeling cost. We conduct a series of experiments to justify that GLAD has the best comprehensive performance balanced between accuracy and efficiency, which implies the advantage in tackling practical problems. The results also reveal that GLAD enables effective anomaly mining and consistently outperforms state-of-the-art methods on both recall and F1 scores.

Face Recognition and Eye Blink Attendance Management System

This research explores the implementation and advantages of utilizing facial recognition technology for automated student attendance management in educational environments. The study outlines a comprehensive system that employs a high-resolution digital camera to detect and recognize students' faces, comparing them with stored images in a database. Successful matches result in real-time attendance logging, enhancing record-keeping and future analysis. The system also accommodates the inclusion of new images when no match is found. By embracing deep learning advancements, the technology offers an efficient alternative to conventional roll-call procedures, enabling automatic head counting in classrooms. The system integrates various face recognition algorithms, leveraging attributes such as shape, color, Local Binary Pattern (LBP), wavelet transforms, and auto-correlation. While challenges like varying lighting conditions, facial expressions, and face orientation are acknowledged, this research underscores the potential of technology to revolutionize attendance management and student engagement in education. Key Words: facial recognition technology, student attendance, automated system, deep learning, real-time logging, attendance management, classroom headcount, face recognition algorithms, record-keeping.

Global microbial water quality data and predictive analytics: Key to health and meeting SDG 6

Microbial water quality is an integral to water security and is directly linked to human health, food safety, and ecosystem services. However, specifically pathogen data and even faecal indicator data (e.g., E. coli), are sparse and scattered, and their availability in different water bodies (e.g., groundwater) and in different socio-economic contexts (e.g., low- and middle-income countries) are inequitable. There is an urgent need to assess and collate microbial data across the world to evaluate the global state of ambient water quality, water treatment, and health risk, as time is running out to meet Sustainable Development Goal (SDG) 6 by 2030. The overall goal of this paper is to illustrate the need and advocate for building a robust and useful microbial water quality database and consortium worldwide that will help achieve SDG 6. We summarize available data and existing databases on microbial water quality, discuss methods for producing new data on microbial water quality, and identify models and analytical tools that utilize microbial data to support decision making. This review identified global datasets (7 databases), and regional datasets for Africa (3 databases), Australia/New Zealand (6 databases), Asia (3 databases), Europe (7 databases), North America (12 databases) and South America (1 database). Data are missing for low- and middle-income countries. Increased laboratory capacity (due to COVID-19 pandemic) and molecular tools can identify potential pollution sources and monitor directly for pathogens. Models and analytical tools can support microbial water quality assessment by making geospatial and temporal inferences where data are lacking. A genomics, information technology (IT), and data revolution is upon us and presents unprecedented opportunities to develop software and devices for real-time logging, automated analysis, standardization, and modelling of microbial data to strengthen knowledge of global water quality. These opportunities should be leveraged for achieving SDG 6 around the world.

Design and Implementation of a Microcontroller-based Weather Acquisition Device

An atmospheric data acquisition device is designed to ease and improve on the current method of acquiring Temperature, Pressure, and Relative Humidity measurement at different altitudes. The proposed work aims to solve the problem of inadequate atmospheric data by monitoring atmospheric weather conditions using sensors while the microcontroller processes the data collected and relays it to the user. This research was carried out at the University of Uyo, between September 2018 and January, 2023. Considering that weather forecasting is of the utmost importance in our current society, the system has been built using a BME280 module for the atmospheric parameters acquisition, an ESP8266 as the microcontroller for Data processing, and a wireless module for processing and transfer of the data from the BME module, a NEO6M GPS module for longitude and latitude, a Li-ion cell to power the components and a TP4056 circuit to recharge the Li-ion cell. A web application Thingspeak.com was incorporated to help the user interact and access the data to enable ease of understanding and real-time logging of the data collected. This work is targeted toward the weather forecasting sector, agricultural sector, and individuals which may wish to gather information about the atmosphere for knowledge consumption. The results show that this device has a good performance for capturing atmospheric parameters for real-time monitoring purposes.

StreamDFP: A General Stream Mining Framework for Adaptive Disk Failure Prediction

We explore machine learning for accurately predicting imminent disk failures and hence providing proactive fault tolerance for modern large-scale storage systems. Current disk failure prediction approaches are mostly offline and assume that the disk logs required for training learning models are available a priori. However, disk logs are often continuously generated as an evolving data stream, in which the statistical patterns vary over time (also known as concept drift). Such a challenge motivates the need of online techniques that perform training and prediction on the incoming stream of disk logs in real time, while being adaptive to concept drift. We first measure and demonstrate the existence of concept drift on various disk models in production. Motivated by our study, we design <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> , a general stream mining framework for disk failure prediction with concept-drift adaptation based on three key techniques, namely online labeling, concept-drift-aware training, and general prediction, with a primary objective of supporting various machine learning algorithms. We extend <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> to support online transfer learning for minority disk models with concept-drift adaptation. Our evaluation shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> improves the prediction accuracy significantly compared to without concept-drift adaptation under various settings, and achieves reasonably high stream processing performance.

Machine Learning Assists Bit Selection and Optimization

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 21299, “Drilling in the Digital Age: Machine-Learning-Assisted Bit Selection and Optimization,” by Peter Batruny, SPE, Hafiz Zubir, and Pete Slagel, SPE, Petronas, et al. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission. _ The project outlined in the complete paper describes machine learning as a powerful tool for bit selection and parameter optimization to improve drilling performance. Machine learning will become a significant part of well planning, design, and operations in the future. The study demonstrates how artificial neural networks (ANNs) can be used to learn from previous operations and influence planning decisions to improve bit performance. Introduction Multiple wells have been drilled in an onshore field in Iraq using different bit designs and with a variety of downhole conditions. To improve the rate of penetration (ROP) in a significant manner, a radical shift in how drill bits are selected, as well as a closer look at bit characteristics, is needed. This work aims to combine analytical and physical models to examine aspects of bit designs previously overlooked. Two mathematical models traditionally have been used for bit selection, the cost per foot (CPF) model and the mechanical specific energy (MSE) model. This work concludes that MSE is more effective than CPF in optimizing ROP and is a valid benchmark for comparing bits. MSE can be used to compare previous bit runs but is limited in predicting ROP for future bit runs. Equipment and Processes Data governance consists of data collection, processing, cleaning, and compilation. Data are collected from American Standard Code for Information Interchange logs in real time and then are processed by removing missing values. The data sets are split by individual bits, with each bit characterized by data compiled from multiple wells in which the bit was run. Throughout this paper, ROP will refer to instantaneous on-bottom ROP. Given the success that ANNs have had, and continue to have, in predicting ROP in the literature, they are used for this work. Once a model is established based on statistical and physical performance, it is used as a base model to run optimization analysis for each bit. This paper examines two methods of optimization, the first being sensitivity analysis based on average ROP. The second optimization method used in this study is a Monte Carlo-style optimization. Once optimal parameters are generated from the sensitivity analysis and Monte Carlo models, they are plotted against the modeled values of offset ROP. This acts as a further verification of the optimal parameters to ensure they are within range of actual historical parameters. The optimal values are then implemented in future wells.

Spray: Streaming Log Parser for Real-Time Analysis

Logs is an important source of data in the field of security analysis. Log messages characterized by unstructured text, however, pose extreme challenges to security analysis. To this end, the first issue to be addressed is how to efficiently parse logs into structured data in real-time. The existing log parsers mostly parse raw log files by batch processing and are not applicable to real-time security analysis. It is also difficult to parse large historical log sets with such parsers. Some streaming log parsers also have some demerits in accuracy and parsing performance. To realize automatic, accurate, and efficient real-time log parsing, we propose Spray, a streaming log parser for real-time analysis. Spray can automatically identify the template of a real-time incoming log and accurately match the log and its template for parsing based on the law of contrapositive. We also improve Spray’s parsing performance based on key partitioning and search tree strategies. We conducted extensive experiments from such aspects as accuracy and performance. Experimental results show that Spray is much more accurate in parsing a variety of public log sets and has higher performance for parsing large log sets.

Mengoptimalkan Keamanan Jaringan Komputer Menggunakan Snort dan Telegram Bot yang Terintegrasi dengan Mikrotik

The implementation of IDS (Intrusion Detection System) snort and telegram bot that can be integrated with a microtic router on the Uppala Villa Nusa Dua computer network is used to detect attack activities and suspicious activity on the Uppala Villa Nusa Dua computer network and to provide notification in real-time logs of suspicious activity on computer network. The research method used is the SPDLC (Security Policy Development Life Cycle) method which has six stages: Identification, Analysis, Design, Implementation, Testing and Evaluation. The software used on the server computer is Snort, WinPcap, Xampp and BASE (Basic Analysis and Security Engine) while for testing a computer network security system using Nmap, Loic and Brutus tools. The results obtained by the implementation of the IDS (Intrusion Detection System) Snort and telegram bot have been successfully implemented and can be integrated with the microtic router. Based on testing conducted after the implementation of the new system, it was found that 95% of the use of snort and telegram bot can optimize the computer network security system at Uppala Villa Nusa Dua.

Learnings From a New Slimhole LWD NMR Technology

This paper presents recent results obtained from a new 4.75-in. logging-while-drilling (LWD) nuclear magnetic resonance (NMR) tool. Data from several wells provided real-time operational insight and new petrophysical learnings. The new LWD technology offers an innovative capability to simultaneously measure NMR T1 and T2 distributions with reduced sensitivity to drilling mud conductivity and a real-time sensor motion-warning system. The real-time T1 and T2 spectra compare favorably with the data set retrieved from the tool memory. The LWD NMR measurements were followed by wireline (WL) NMR logging for comparison in three wells. In carbonate reservoirs, the main objective was to evaluate the new NMR tool’s capability in resolving carbonate pore size with slow NMR relaxation rates. Another set of NMR logs contained time-lapse repeat passes in a well drilled with oil-based mud (OBM) that traversed a clastic reservoir. The sequence of measurements identified rock types and provided a unique insight into the mud filtrate invasion process. In a carbonate reservoir, the LWD NMR real-time partial porosity estimates were in excellent agreement with the core-calibrated WL micro-, meso-, and macropore volumes. Another carbonate formation contained high-permeability layers embedded in a microporous rock, where the real-time LWD NMR logs successfully located the high-quality rock zones, later verified by WL logging. In the clastic reservoir, the while-drilling LWD NMR data indicated native light hydrocarbons, whereas the subsequent LWD reaming and WL NMR passes showed a displacement of native hydrocarbons by oil-based mud filtrate (OBMF). The native hydrocarbon signature observed by the LWD NMR tool was in good agreement with the mud gas log. OBMF invasion occurred shortly after drilling, indicated by the differences observed in the LWD NMR relogged data acquired several hours after the while-drilling pass. The WL NMR data, logged about 4 days after drilling, showed advanced stages of OBMF invasion, including formation water displacement and wettability changes in intermediate and large pores. Finally, the environmental noise remained low in the LWD NMR data set acquired in a well in which the mud salinity changed by several folds, indicating that mud salinity had little effect on the quality of LWD NMR logs in slim holes. The new slimhole LWD NMR technology demonstrated its robust capability to provide T1 and T2 logs through several examples. For the first time, a time-lapse comparison of NMR logs showed that OBMF invasion could occur in silty sands with high capillary-bound fluid fractions.

VNGuarder: An Internal Threat Detection Approach for Virtual Network in Cloud Computing Environment

Edge-assisted Internet of things applications often need to use cloud virtual network services to transmit data. However, the internal threats such as illegal management and configuration to cloud platform intentionally or unintentionally will lead to virtual network security problems such as malicious changes of user network and hijacked data flow. It will eventually affect edge-assisted Internet of things applications. We propose a virtual network internal threat detection method called VNGuarder in a cloud computing environment, which can effectively monitor whether the virtual network configuration of legitimate users under the IaaS cloud platform has been maliciously changed or destroyed by insiders. First, based on the life cycle of cloud virtual network services, we summarized two types of internal attacks involving illegal use of virtualization management tools and illegal invocation of virtual network-related processes. Second, based on normal behavior of tenants, a hierarchical trusted call correlation scheme is proposed to provide a basis for discovering that insiders illegally call virtualized management tools and virtual network-related processes on the controller node of the cloud platform or the network node and compute node. Third, a trace-enable mechanism combining real-time monitoring and log analysis is introduced. By collecting and recording the complete call process of virtual network management and configuration in the cloud platform, and comparing it with the result of the hierarchical trusted call correlation, abnormal operations can be reported to the tenants in time. Comprehensive simulation experiments on the Openstack platform show that VNGuarder can effectively detect illegal management and configuration of virtual networks by insiders without significantly affecting the creation time of tenant networks and the utilization of CPU and memory.

Assessing temporal eating pattern in free living humans through the myCircadianClock app.

The quality and quantity of nutrition impact health. However, chrononutrition, the timing, and variation of food intake in relation to the daily sleep-wake cycle are also important contributors to health. This has necessitated an urgent need to measure, analyze, and optimize eating patterns to improve health and manage disease. While written food journals, questionnaires, and 24-hour dietary recalls are acceptable methods to assess the quantity and quality of energy consumption, they are insufficient to capture the timing and day-to-day variation of energy intake. Smartphone applications are novel methods for information-dense real-time food and beverage tracking. Despite the availability of thousands of commercial nutrient apps, they almost always ignore eating patterns, and the raw real-time data is not available to researchers for monitoring and intervening in eating patterns. Our lab developed a smartphone app called myCircadianClock (mCC) and associated software to enable long-term real-time logging that captures temporal components of eating patterns. The mCC app runs on iOS and android operating systems and can be used to track multiple cohorts in parallel studies. The logging burden is decreased by using a timestamped photo and annotation of the food/beverage being logged. Capturing temporal data of consumption in free-living individuals over weeks/months has provided new insights into diverse eating patterns in the real world. This review discusses (1) chrononutrition and the importance of understanding eating patterns, (2) the myCircadianClock app, (3) validation of the mCC app, (4) clinical trials to assess the timing of energy intake, and (5) strengths and limitations of the mCC app.

Blockchain Analytics - Real-time Log Management in Healthcare

With the growing dependence on information technologies in healthcare organizations, it is crucial to ensure data security since its sensitiveness. To guarantee this requirement, a system was developed consisting of a private blockchain network designed to support the recording of hospital logs and an interface to facilitate data analysis. This article covers the development of a private blockchain solution. For its development were used Hyperledger technologies and Sockets.io. Apart from that, web technologies that were used to interface real-time logs. The objective of the developed solution is to allow the visualization of logs in real time, where analysis methods have applied.

A Method for Intrusion Detection in Smart Grid

The advent and development of the smart grid have introduced a number of opportunities for improving efficiencies and overall performance. Along with the opportunities, each technology wave brings with it new opportunities for attackers to perpetrate damage, that in the smart grid scenario afflict electricity networks i.e. a cyber physical environment. In this paper we propose a method aimed to detect intrusion in smart grid by exploiting supervised machine learning techniques. By real-time log analysis, obtained from SCADA systems, widely exploited in a plethora of industrial applications, we extract a set of 26 features to build seven different machine learning models. Experimental analysis performed with 7 machine learning algorithms shows the effectiveness of the proposed method.