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- 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if Machine Learning (ML) models are used to find anomalies, they need to be regularly trained/updated to avoid becoming outdated. In an evolving environment, a ML model with outdated training might put at risk the organization it was supposed to defend.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.
- 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.
- A Scalable Framework to Predict Bitcoin Price Using Support Vector MachinePublication . Monteiro, Stéphane; Oliveira, Diogo; António, João; Henriques, João; Martins, Pedro; Wanzeller, Cristina; Caldeira, FilipeStock analysts have been predicting other stocks prices in financial markets by understanding their patterns. Bitcoin is an example of cryptocurrency that has grow enormously since 2020 despite being in the market since 2009. However, cryptocurrencies are volatile and sensitive to thousands of factors and consequently is complex for humans to make predictions even more when those predictions should occur in a daily basis, requiring hence a significant effort. Due to this fact, investor profiles prefer long-term investments which can constraint their revenues. To overcome the aforementioned scenario this work proposes a scalable framework relying in Support Vector Machine (SVM) algorithm to predict the price of bitcoin by automatically collecting and cleaning the data directly from the Web to process the dataset as input for training. This framework can also leverage new business models at scale by assisting the investors aiming realize its value in short periods in a continuous fashion.