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- An Advertising Real-Time Intelligent and Scalable Framework for Profiling Customers EmotionsPublication . Alves, Leandro; Oliveira, Pedro; Henriques, João; Bernardo, Marco V.; Wanzeller, Cristina; Caldeira, FilipeThe advertising industry is continuously looking up for effective ways to communicate to customers to impact their purchasing. Usually, profiling them is a time-consuming offline activity. Therefore, it becomes necessary to reduce costs and time to address consumers’ needs. This work proposes a scalable framework enabled by a Machine Learning (ML) model to profile customers to identify their emotions to help to drive campaigns. A multi-platform mobile application continuously profiles consumers crossing the front stores. Profiling customers according to their age and hair color, the color of their eyes, and emotions (e.g. happiness, sadness, disgust, fear) will help companies to make the most suitable advertisement (e.g. to predict whether the advertising tones on the front store are the adequate ones). All that data are made available in web portal dashboards, wherein subscribers can take their analysis. Such results from the analysis data help them to identify tendencies regarding the culture and age, and drive companies to fit front stores accordingly (e.g. to discover the right tones for the season). This framework can help to develop new innovative cost-effective business models at scale by driving in real-time the advertisements to a huge number of consumers to maximize their impact and centralizing the data collected from a large number of stores to design future campaigns.
- An automated closed-loop framework to enforce security policies from anomaly detectionPublication . Henriques, João; Caldeira, Filipe; Cruz, Tiago; Simões, PauloDue to the growing complexity and scale of IT systems, there is an increasing need to automate and streamline routine maintenance and security management procedures, to reduce costs and improve productivity. In the case of security incidents, the implementation and application of response actions require significant efforts from operators and developers in translating policies to code. Even if Machine Learning (ML) models are used to find anomalies, they need to be regularly trained/updated to avoid becoming outdated. In an evolving environment, a ML model with outdated training might put at risk the organization it was supposed to defend. To overcome those issues, in this paper we propose an automated closed-loop process with three stages. The first stage focuses on obtaining the Decision Trees (DT) that classify anomalies. In the second stage, DTs are translated into security Policies as Code based on languages recognized by the Policy Engine (PE). In the last stage, the translated security policies feed the Policy Engines that enforce them by converting them into specific instruction sets. We also demonstrate the feasibility of the proposed framework, by presenting an example that encompasses the three stages of the closed-loop process. The proposed framework may integrate a broad spectrum of domains and use cases, being able for instance to support the decide and the act stages of the ETSI Zero-touch Network & Service Management (ZSM) framework.
- Integração ubíqua - Proposta de modelo de integraçãoPublication . Henriques, João; Tomé, Paulo Rogério PerfeitoO paradigma de sistemas heterogéneos vem requerendo a implementação de novas abordagens que possibilitem o seu funcionamento como um todo. Atualmente, para se alcançar um nível de integração satisfatório, é necessário que cada um dos sistemas disponha no seu seio, de forma nativa, o conjunto de instruções que permitam invocar as funcionalidades que são disponibilizadas pelos demais sistemas. Os sistemas dispõem de capacidades de interligação distintas, baseando-se em protocolos de comunicação, que de todo não possuem as características que garantam o máximo partido das capacidades de integração. Esta dissertação pretende contribuir, com o seu trabalho, para a de nição de uma nova arquitetura passível de aplicação a qualquer sistema existente, visando reduzir a di culdade de desenho e implementação de soluções de integração. Esta nova abordagem tem como intuito agilizar e remover a complexidade relacionada com a de nição e com a implementação de soluções distribuídas, possibilitando aos diversos sistemas que implementem este modelo, um elevado grau de integração atrav és da utilização de um protocolo comum com benefícios em termos de redução de complexidade, tempo e custo de implementação. A arquitetura suportar-se-á sobre princípios de software livre, suportando-se nas tecnologias existentes, recorrendo a standards, como o XML, para a estruturação de uma linguagem de programação, que garanta o intercâmbio de instruções entre os diversos sistemas, para além da sua utilização já habitual na comunicação de dados. A de nição e o desenho desta arquitetura serão realizados recorrendo às metodologias de modelação UML. Um sistema que se encontre suportado nesta arquitetura, publicará aos restantes sistemas as funcionalidades que alberga, aceitando sobre uma interface o conjunto de instruções que utilizam as funcionalidades disponibilizadas pelo sistema. Esta arquitetura possibilitará a estruturação de diferentes tipologias relacionadas com a computação distribuída.
- An Intelligent and Scalable IoT Monitoring Framework for Safety in Civil Construction WorkspacesPublication . Ferreira, Carolina; Correia, Luciano; Lopes, Manuel; Henriques, João; Martins, Pedro; Wanzeller, Cristina; Caldeira, FilipeKeeping civil construction workers safe is an important challenge due to working conditions and low technological support due to the inherent costs. This work surveys the literature and proposes a scalable framework for monitoring workers to minimize the response time with real-time warnings in hazardous situations or safety incidents. From the literature, it was possible to devise a gap in business addressing this problem. To address this problem, this work proposes an IoT scalable framework able to scale to a large number of civil construction companies with a large number of workers in order. The results from this work demonstrate the feasibility of the proposed framework and the low cost of the IoT solution and the scalability of the framework offers the opportunity to leverage new innovative business models capable to leverage their revenues.
- Intrusion and anomaly detection for the next-generation of industrial automation and control systemsPublication . Rosa, Luis; Cruz, Tiago; Freitas, Miguel Borges de; Quitério, Pedro; Henriques, João; Caldeira, Filipe; Monteiro, Edmundo; Simões, PauloThe next-generation of Industrial Automation and Control Systems (IACS) and Supervisory Control and Data Acquisition (SCADA) systems pose numerous challenges in terms of cybersecurity monitoring. We have been witnessing the convergence of OT/IT networks, combined with massively distributed metering and control scenarios such as smart grids. Larger and geographically widespread attack surfaces, and inherently more data to analyse, will become the norm. Despite several advances in recent years, domain-specific security tools have been facing the challenges of trying to catch up with all the existing security flaws from the past, while also accounting for the specific needs of the next-generation of IACS. Moreover, the aggregation of multiple techniques and sources of information into a comprehensive approach has not been explored in depth. Such a holistic perspective is paramount since it enables a global and enhanced analysis enabled by the usage, combination and aggregation of the outputs from multiple sources and techniques. This paper starts by providing a review of the more recent anomaly detection techniques for SCADA systems, focused on both theoretical machine learning approaches and complete frameworks. Afterwards, it proposes a complete framework for an Intrusion and Anomaly Detection System (IADS) composed of specific detection probes, an event processing layer and a core anomaly detection component, amongst others. Finally, the paper presents an evaluation of the framework within a large-scale hybrid testbed, and a comparison of different anomaly detection scenarios based on various machine learning techniques.
- A Scalable Smart Lighting Framework to Save EnergyPublication . Rebelo, João; Rodrigues, Ricardo; Henriques, João; Gonçalves Cardoso, Filipe; Wanzeller, Cristina; Caldeira, FilipeIn the past few decades, the urbanization area increased significantly, requiring enhanced services and applications to improve the lifestyle of its citizens. Lighting is one of the most relevant infrastructures due to its impact on modern societies, but it is also complex to manage them in cities since it involves a massive number of widespread posts and is costly as the result of the consumption of significant amounts of energy. In that regard, this work proposes a scalable framework to manage a significant huge number of lamp posts. Its purpose is to give support to collecting large amounts of sensor data to help to analyze and efficiently fit the light intensity level to the space the posts are covering. Luminosity sensors are used to optimize the intensity of light needed in the urban areas. The proposed framework explores the concept of smart cities by combining the data collected from sensors plugged into IoT (Internet-ofThings) devices. The proposed framework offers the capability to extend and integrate new services to different domains with each other which enhances the quality and performance of urban services. To demonstrate the feasibility of the framework, a simulation was put in place.
- Combining K-Means and XGBoost Models for Anomaly Detection Using Log DatasetsPublication . Henriques, João; Caldeira, Filipe; Cruz, Tiago; Simões, PauloAbstract: Computing and networking systems traditionally record their activity in log files, which have been used for multiple purposes, such as troubleshooting, accounting, post-incident analysis of security breaches, capacity planning and anomaly detection. In earlier systems those log files were processed manually by system administrators, or with the support of basic applications for filtering, compiling and pre-processing the logs for specific purposes. However, as the volume of these log files continues to grow (more logs per system, more systems per domain), it is becoming increasingly difficult to process those logs using traditional tools, especially for less straightforward purposes such as anomaly detection. On the other hand, as systems continue to become more complex, the potential of using large datasets built of logs from heterogeneous sources for detecting anomalies without prior domain knowledge becomes higher. Anomaly detection tools for such scenarios face two challenges. First, devising appropriate data analysis solutions for effectively detecting anomalies from large data sources, possibly without prior domain knowledge. Second, adopting data processing platforms able to cope with the large datasets and complex data analysis algorithms required for such purposes. In this paper we address those challenges by proposing an integrated scalable framework that aims at efficiently detecting anomalous events on large amounts of unlabeled data logs. Detection is supported by clustering and classification methods that take advantage of parallel computing environments. We validate our approach using the the well known NASA Hypertext Transfer Protocol (HTTP) logs datasets. Fourteen features were extracted in order to train a k-means model for separating anomalous and normal events in highly coherent clusters. A second model, making use of the XGBoost system implementing a gradient tree boosting algorithm, uses the previous binary clustered data for producing a set of simple interpretable rules. These rules represent the rationale for generalizing its application over a massive number of unseen events in a distributed computing environment. The classified anomaly events produced by our framework can be used, for instance, as candidates for further forensic and compliance auditing analysis in security management.
- Analysis and real-time data of meteorologic impact on home solar energy harvestingPublication . Ferreira, João; Lourenço, Ismael; Henriques, João; Pires, Ivan Miguel; Caldeira, Filipe; Wanzeller, CristinaSolar energy production increased in the world from 0 TWh in 1965 to 724.09 TWh in 2019. Solar energy is adopted as a source for residential renewable energy sources because, besides Biomass sources, it’s the only one that can be installed and maintained at home. Operating efficiency is an important consideration when evaluating the application of photovoltaic panels (PV) technology. A real-time system monitoring is required to analyse the current production and understand the impact of the weather conditions on PV production. This paper extends the literature on the residence solar energy harvesting subject, by providing a scalable architecture that can be used as starting point on data analysis on PV panels efficiency and how weather conditions impact energy production. A dataset was collected related to PV panel energy production, the residence energy consumption and that’s reading weather conditions. Wind intensity and direction, temperature, precipitation, humidity, atmospheric pressure and radiation were weather conditions analysed. Moreover, this data was analysed and interpreted in order to evaluate the pros and cons of the architecture as well as how the weather impacted the energy production.
- A Cost-Effective Framework for Monitoring Disaster Recovery InfrastructuresPublication . Rocha, Júlio; Lucas, Marco; Figueiredo, Ricardo; Henriques, João; Bernardo, Marco V.; Wanzeller, Cristina; Caldeira, FilipeKeeping Disaster Recovery Infrastructures (DRI) operational is vital in case of incidents. Notwithstanding, continuously monitoring them keeps a costly activity. Therefore, it is essential to have cost-effective solutions while maintaining a continuous. In that aim, this work proposes a cost-effective framework for monitoring DRI supported by an Internet of Things (IoT) device collecting data from their sensors strategically installed in the facilities to protect. In case of incidents, the framework triggers the alerts. A mobile application presents graphically, in real-time, the collected data from sensors. The physical experimentation and achieved results demonstrate the effectiveness of the framework to protect DRI. The proposed framework enabled by software to the different layers (IoT, middleware, and mobile application), and the hardware with its schematic, can help to develop innovative business models for managing DRI. The prototype of the framework produced a large dataset that can help future research on finding anomalies.
- COVID-19 Next Day Trend ForecastPublication . Costa, Marcelo; Rodrigues, Margarida; Baptista, Pedro; Henriques, João; Pires, Ivan Miguel; Wanzeller, Cristina; Caldeira, FilipeHistorically, weather conditions are depicted as an essential factor to be considered in predicting variation infections due to respiratory diseases, including influenza and Severe Acute Respiratory Syndrome SARS-CoV-2, best known as COVID-19. Predicting the number of cases will contribute to plan human and non-human resources in hospital facilities, including beds, ventilators, and support policy decisions on sanitary population warnings, and help to provision the demand for COVID-19 tests. In this work, an integrated framework predicts the number of cases for the upcoming days by considering the COVID-19 cases and temperature records supported by a kNN algorithm.