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Isabel Praça
GECAD—Knowledge Engineering and Decision Support Research Centre, School of Engineering, Polytechnic of Porto (ISEP/IPP), 4050-535 Porto, Portugal

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Journal article
Published: 17 August 2021 in Electronics
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This paper explores the concept of the local energy markets and, in particular, the need for trust and security in the negotiations necessary for this type of market. A multi-agent system is implemented to simulate the local energy market, and a trust model is proposed to evaluate the proposals sent by the participants, based on forecasting mechanisms that try to predict their expected behavior. A cyber-attack detection model is also implemented using several supervised classification techniques. Two case studies were carried out, one to evaluate the performance of the various classification methods using the IoT-23 cyber-attack dataset; and another one to evaluate the performance of the developed trust mode.

ACS Style

Rui Andrade; Sinan Wannous; Tiago Pinto; Isabel Praça. Extending a Trust model for Energy Trading with Cyber-Attack Detection. Electronics 2021, 10, 1975 .

AMA Style

Rui Andrade, Sinan Wannous, Tiago Pinto, Isabel Praça. Extending a Trust model for Energy Trading with Cyber-Attack Detection. Electronics. 2021; 10 (16):1975.

Chicago/Turabian Style

Rui Andrade; Sinan Wannous; Tiago Pinto; Isabel Praça. 2021. "Extending a Trust model for Energy Trading with Cyber-Attack Detection." Electronics 10, no. 16: 1975.

Communication
Published: 21 June 2021 in Sensors
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The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

ACS Style

Hoon Ko; Kwangcheol Rim; Isabel Praça. Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System. Sensors 2021, 21, 4237 .

AMA Style

Hoon Ko, Kwangcheol Rim, Isabel Praça. Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System. Sensors. 2021; 21 (12):4237.

Chicago/Turabian Style

Hoon Ko; Kwangcheol Rim; Isabel Praça. 2021. "Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System." Sensors 21, no. 12: 4237.

Conference paper
Published: 29 March 2021 in Advances in Intelligent Systems and Computing
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Investigating efficiently the data collected from a system’s activity can help to detect malicious attempts and better understand the context behind past incident occurrences. Nowadays, several solutions can be used to monitor system activities to detect probable abnormalities and malfunctions. However, most of these systems overwhelm their users with vast amounts of information, making it harder for them to perceive incident occurrences and their context. Our approach combines a dynamic and intuitive user interface with Machine Learning forecasts to provide an intelligent investigation tool that facilitates the security operator’s work. Our system can also act as an enhanced and fully automated decision support mechanism that provides suggestions about possible incident occurrences.

ACS Style

Inês Macedo; Sinan Wanous; Nuno Oliveira; Orlando Sousa; Isabel Praça. A Tool to Support the Investigation and Visualization of Cyber and/or Physical Incidents. Advances in Intelligent Systems and Computing 2021, 130 -140.

AMA Style

Inês Macedo, Sinan Wanous, Nuno Oliveira, Orlando Sousa, Isabel Praça. A Tool to Support the Investigation and Visualization of Cyber and/or Physical Incidents. Advances in Intelligent Systems and Computing. 2021; ():130-140.

Chicago/Turabian Style

Inês Macedo; Sinan Wanous; Nuno Oliveira; Orlando Sousa; Isabel Praça. 2021. "A Tool to Support the Investigation and Visualization of Cyber and/or Physical Incidents." Advances in Intelligent Systems and Computing , no. : 130-140.

Conference paper
Published: 18 February 2021 in Transactions on Petri Nets and Other Models of Concurrency XV
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Cyber-attacks are becoming more sophisticated and thereby more difficult to detect. This is a concern to all, but even more to Critical Infrastructures, like health organizations. A Cyber Threat Monitoring System (CTMS), providing a global approach to detect and analyze cyber-threats for health infrastructures is proposed by combining a set of solutions from Airbus CyberSecurity with a machine learning pipeline to improve detection and provide awareness from cyber side to a more global approach that will combine them with physical incidents. The work is being carried out in the scope of SAFECARE project. In this work, we present the CTMS architecture and present our experimental findings with ensemble learning methods for intrusion detection. Several parameters of six different ensemble methods are optimized, using Grid Search and Bayesian Search approaches, in order to detect intrusions as soon as they occur. Then, after the determination of best set of parameters for each algorithm, the attack detection performance of these six different ensemble algorithms using the CICIDS 2017 dataset are calculated and discussed. The results obtained identified Random Forest, LightGBM and Decision Trees as the best algorithms, with no significant difference in the performance, using a 95% confidence interval.

ACS Style

Eva Maia; Bruno Reis; Isabel Praça; Adrien Becue; David Lancelin; Samantha Dauguet Demailly; Orlando Sousa. Cyber Threat Monitoring Systems - Comparing Attack Detection Performance of Ensemble Algorithms. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 12618, 31 -47.

AMA Style

Eva Maia, Bruno Reis, Isabel Praça, Adrien Becue, David Lancelin, Samantha Dauguet Demailly, Orlando Sousa. Cyber Threat Monitoring Systems - Comparing Attack Detection Performance of Ensemble Algorithms. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; 12618 ():31-47.

Chicago/Turabian Style

Eva Maia; Bruno Reis; Isabel Praça; Adrien Becue; David Lancelin; Samantha Dauguet Demailly; Orlando Sousa. 2021. "Cyber Threat Monitoring Systems - Comparing Attack Detection Performance of Ensemble Algorithms." Transactions on Petri Nets and Other Models of Concurrency XV 12618, no. : 31-47.

Journal article
Published: 13 February 2021 in Applied Sciences
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With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are shared continuously across the network, making it susceptible to an attack that can compromise data confidentiality, integrity, and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform the timely detection of malicious events through the inspection of network traffic or host-based logs. Many machine learning techniques have proven to be successful at conducting anomaly detection throughout the years, but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP), and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, which only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes suggest that anomaly detection can be better addressed from a sequential perspective. The LSTM is a highly reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and an f1-score of 91.66%.

ACS Style

Nuno Oliveira; Isabel Praça; Eva Maia; Orlando Sousa. Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems. Applied Sciences 2021, 11, 1674 .

AMA Style

Nuno Oliveira, Isabel Praça, Eva Maia, Orlando Sousa. Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems. Applied Sciences. 2021; 11 (4):1674.

Chicago/Turabian Style

Nuno Oliveira; Isabel Praça; Eva Maia; Orlando Sousa. 2021. "Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems." Applied Sciences 11, no. 4: 1674.

Journal article
Published: 08 February 2021 in Processes
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In recent years Local Energy Markets (LEM) have emerged as an innovative and versatile energy trade solution. They bring benefits when renewable energy sources are used and are more flexible for consumers. There are, however, security concerns that put the feasibility of the local energy market at risk. One of these security challenges is the integrity of data in the smart-grid that supports the local market. In this article the LEM and the types of attacks that can have a negative impact on it are presented, and a security mechanism based on a trust model is proposed. A case study is elaborated using a multi-agent system called Local Energy Market Multi-Agent System (LEMMAS), capable of simulating the LEM and testing the proposed security mechanism.

ACS Style

Rui Andrade; Isabel Praça; Sinan Wannous; Sergio Ramos. The Impact of Attacks in LEM and Prevention Measures Based on Forecasting and Trust Models. Processes 2021, 9, 314 .

AMA Style

Rui Andrade, Isabel Praça, Sinan Wannous, Sergio Ramos. The Impact of Attacks in LEM and Prevention Measures Based on Forecasting and Trust Models. Processes. 2021; 9 (2):314.

Chicago/Turabian Style

Rui Andrade; Isabel Praça; Sinan Wannous; Sergio Ramos. 2021. "The Impact of Attacks in LEM and Prevention Measures Based on Forecasting and Trust Models." Processes 9, no. 2: 314.

Article
Published: 04 February 2021 in Artificial Intelligence Review
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This survey paper discusses opportunities and threats of using artificial intelligence (AI) technology in the manufacturing sector with consideration for offensive and defensive uses of such technology. It starts with an introduction of Industry 4.0 concept and an understanding of AI use in this context. Then provides elements of security principles and detection techniques applied to operational technology (OT) which forms the main attack surface of manufacturing systems. As some intrusion detection systems (IDS) already involve some AI-based techniques, we focus on existing machine-learning and data-mining based techniques in use for intrusion detection. This article presents the major strengths and weaknesses of the main techniques in use. We also discuss an assessment of their relevance for application to OT, from the manufacturer point of view. Another part of the paper introduces the essential drivers and principles of Industry 4.0, providing insights on the advent of AI in manufacturing systems as well as an understanding of the new set of challenges it implies. AI-based techniques for production monitoring, optimisation and control are proposed with insights on several application cases. The related technical, operational and security challenges are discussed and an understanding of the impact of such transition on current security practices is then provided in more details. The final part of the report further develops a vision of security challenges for Industry 4.0. It addresses aspects of orchestration of distributed detection techniques, introduces an approach to adversarial/robust AI development and concludes with human–machine behaviour monitoring requirements.

ACS Style

Adrien Bécue; Isabel Praça; João Gama. Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities. Artificial Intelligence Review 2021, 54, 3849 -3886.

AMA Style

Adrien Bécue, Isabel Praça, João Gama. Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities. Artificial Intelligence Review. 2021; 54 (5):3849-3886.

Chicago/Turabian Style

Adrien Bécue; Isabel Praça; João Gama. 2021. "Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities." Artificial Intelligence Review 54, no. 5: 3849-3886.

Journal article
Published: 03 February 2021 in Sustainability
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This study proposes a Secure Energy Trading Model design based on a Blockchain is an attempt to overcome the weak security and instability of the current energy trading system. The focal point of the design lies in the user-security features of the model, such as user authentication and identification, and the blockchain that every transaction goes through. The user-security feature provides a safer system for peer-to-peer energy trade, and the blockchain technology ensures the reliability of the trading system. Furthermore, the Secure Energy Trading Model supports a decentralized data control mechanism as a future measure for handling vast amounts of data created by IoT.

ACS Style

Hoon Ko; Isabel Praca. Design of a Secure Energy Trading Model Based on a Blockchain. Sustainability 2021, 13, 1634 .

AMA Style

Hoon Ko, Isabel Praca. Design of a Secure Energy Trading Model Based on a Blockchain. Sustainability. 2021; 13 (4):1634.

Chicago/Turabian Style

Hoon Ko; Isabel Praca. 2021. "Design of a Secure Energy Trading Model Based on a Blockchain." Sustainability 13, no. 4: 1634.

Preprint
Published: 14 December 2020
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With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

ACS Style

Nuno Oliveira; Isabel Praça; Eva Maia; Orlando Sousa. Intelligent Cyber-Attack Detection and Classification for Network-based Intrusion Detection Systems. 2020, 1 .

AMA Style

Nuno Oliveira, Isabel Praça, Eva Maia, Orlando Sousa. Intelligent Cyber-Attack Detection and Classification for Network-based Intrusion Detection Systems. . 2020; ():1.

Chicago/Turabian Style

Nuno Oliveira; Isabel Praça; Eva Maia; Orlando Sousa. 2020. "Intelligent Cyber-Attack Detection and Classification for Network-based Intrusion Detection Systems." , no. : 1.

Conference paper
Published: 10 September 2020 in Advances in Intelligent Systems and Computing
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During human evolution emotion expression became an important social tool that contributed to the complexification of societies. Human-computer interaction is commonly present in our daily life, and the industry is struggling for solutions that can analyze human emotions, to improve workers safety and security, as well as processes optimization. In this work we present a software built using the transfer-learning technique on a deep learning model, and conclude about how it can classify human emotions through facial expression analysis. A Convolutional Neuronal Network model was trained and used in a web application. Several tools were created to facilitate the software development process, including the training and validation processes. Data was collected by combining several facial expression emotion databases. Software evaluation revealed an accuracy in identifying the correct emotions close to 80% .

ACS Style

Ricardo Rocha; Isabel Praça. FullExpression Using Transfer Learning in the Classification of Human Emotions. Advances in Intelligent Systems and Computing 2020, 72 -81.

AMA Style

Ricardo Rocha, Isabel Praça. FullExpression Using Transfer Learning in the Classification of Human Emotions. Advances in Intelligent Systems and Computing. 2020; ():72-81.

Chicago/Turabian Style

Ricardo Rocha; Isabel Praça. 2020. "FullExpression Using Transfer Learning in the Classification of Human Emotions." Advances in Intelligent Systems and Computing , no. : 72-81.

Conference paper
Published: 06 July 2020 in Communications in Computer and Information Science
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This paper explores the concept of the Local Energy Market and, in particular, the need for Trust in the negotiations necessary for this type of market. A multi-agent system is implemented to simulate the Local Energy Market, and a Trust model is proposed to evaluate the proposals sent by the participants, based on forecasting mechanisms that try to predict the expected behavior of the participant. A case study is carried out with several participants who submit false negotiation proposals to assess the ability of the proposed Trust model to correctly evaluate these participants. The results obtained demonstrate that such an approach has the potential to meet the needs of the local market.

ACS Style

Rui Andrade; Tiago Pinto; Isabel Praça. Trust Model for a Multi-agent Based Simulation of Local Energy Markets. Communications in Computer and Information Science 2020, 183 -194.

AMA Style

Rui Andrade, Tiago Pinto, Isabel Praça. Trust Model for a Multi-agent Based Simulation of Local Energy Markets. Communications in Computer and Information Science. 2020; ():183-194.

Chicago/Turabian Style

Rui Andrade; Tiago Pinto; Isabel Praça. 2020. "Trust Model for a Multi-agent Based Simulation of Local Energy Markets." Communications in Computer and Information Science , no. : 183-194.

Conference paper
Published: 06 July 2020 in Communications in Computer and Information Science
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This paper addresses energy management and security having as basis sensing and monitoring of cyber-physical infrastructure of consumers and prosumers, and their participation in the Local Energy Market (LEM). The vision is to create a layered multi-agent framework that brings a complete view of the cyber-physical system of LEM participants, and provides optimization and control of energy for said participants. The proposed system is separated into a Market layer and a Cyber-Physical layer, each of them providing different services. The cyber-physical layer, represented by SMARTERCtrol system, provides Data Monitoring and Optimized Energy Control of individual building resources. The Market layer, represented by LEM Multi-Agent System, provides Negotiation, Forecasting, and Trust Evaluation. Both systems work together to provide and integrate a tool for simulation and control of LEM.

ACS Style

Rui Andrade; Sandra Garcia-Rodriguez; Isabel Praca; Zita Vale. A Two Tier Architecture for Local Energy Market Simulation and Control. Communications in Computer and Information Science 2020, 302 -313.

AMA Style

Rui Andrade, Sandra Garcia-Rodriguez, Isabel Praca, Zita Vale. A Two Tier Architecture for Local Energy Market Simulation and Control. Communications in Computer and Information Science. 2020; ():302-313.

Chicago/Turabian Style

Rui Andrade; Sandra Garcia-Rodriguez; Isabel Praca; Zita Vale. 2020. "A Two Tier Architecture for Local Energy Market Simulation and Control." Communications in Computer and Information Science , no. : 302-313.

Journal article
Published: 28 June 2020 in Applied Sciences
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In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.

ACS Style

Adrien Bécue; Eva Maia; Linda Feeken; Philipp Borchers; Isabel Praça. A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future. Applied Sciences 2020, 10, 4482 .

AMA Style

Adrien Bécue, Eva Maia, Linda Feeken, Philipp Borchers, Isabel Praça. A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future. Applied Sciences. 2020; 10 (13):4482.

Chicago/Turabian Style

Adrien Bécue; Eva Maia; Linda Feeken; Philipp Borchers; Isabel Praça. 2020. "A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future." Applied Sciences 10, no. 13: 4482.

Journal article
Published: 08 May 2020 in Neurocomputing
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This paper presents three ensemble learning models for short term load forecasting. Machine learning has evolved quickly in recent years, leading to novel and advanced models that are improving the forecasting results in multiple fields. However, in highly dynamic fields such as power and energy systems, dealing with the fast acquisition of large amounts of data from multiple data sources and taking advantage from the correlation between the multiple available variables is a challenging task, for which current models are not prepared. Ensemble learning is bringing promising results in this sense, as, by combining the results and use of multiple learners, is able to find new ways for current learning models to be used and optimized. In this paper three ensemble learning models are developed and the respective results compared: gradient boosted regression trees, random forests and an adaptation of Adaboost. Results for electricity consumption forecasting in hour-ahead are presented using a case-study based on real data from an office building. Results show that the adapted Adaboost model outperforms the reference models for hour-ahead load forecasting.

ACS Style

Tiago Pinto; Isabel Praça; Zita Vale; Jose Silva. Ensemble learning for electricity consumption forecasting in office buildings. Neurocomputing 2020, 423, 747 -755.

AMA Style

Tiago Pinto, Isabel Praça, Zita Vale, Jose Silva. Ensemble learning for electricity consumption forecasting in office buildings. Neurocomputing. 2020; 423 ():747-755.

Chicago/Turabian Style

Tiago Pinto; Isabel Praça; Zita Vale; Jose Silva. 2020. "Ensemble learning for electricity consumption forecasting in office buildings." Neurocomputing 423, no. : 747-755.

Conference paper
Published: 17 April 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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During the last decade network infrastructures have been in a constant evolution. And, at the same time, attacks and attack vectors become increasingly sophisticated. Hence, networks contain a lot of different features that can be used to identify attacks. Machine learning are particularly useful at dealing with large and varied datasets, which are crucial to develop an accurate intrusion detection system. Thus, the huge challenge that intrusion detection represents can be supported by machine learning techniques. In this work, several feature selection and ensemble methods are applied to the recent CICIDS2017 dataset in order to develop valid models to detect intrusions as soon as they occur. Using permutation importance the original 69 features in the dataset have been reduced to only 10 features, which allows the reduction of models execution time, and leads to faster intrusion detection systems. The reduced dataset was evaluated using Random Forest algorithm, and the obtained results show that the optimized dataset maintains a high detection rate performance.

ACS Style

Bruno Reis; Eva Maia; Isabel Praça. Selection and Performance Analysis of CICIDS2017 Features Importance. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 56 -71.

AMA Style

Bruno Reis, Eva Maia, Isabel Praça. Selection and Performance Analysis of CICIDS2017 Features Importance. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():56-71.

Chicago/Turabian Style

Bruno Reis; Eva Maia; Isabel Praça. 2020. "Selection and Performance Analysis of CICIDS2017 Features Importance." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 56-71.

Conference paper
Published: 01 September 2019 in 2019 16th International Conference on the European Energy Market (EEM)
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ACS Style

Isabel Praça; Sérgio Ramos; Rui Andrade; Allon Soares Da Silva; Everthon Taghori Sica. Analysis and Simulation of Local Energy Markets. 2019 16th International Conference on the European Energy Market (EEM) 2019, 1 .

AMA Style

Isabel Praça, Sérgio Ramos, Rui Andrade, Allon Soares Da Silva, Everthon Taghori Sica. Analysis and Simulation of Local Energy Markets. 2019 16th International Conference on the European Energy Market (EEM). 2019; ():1.

Chicago/Turabian Style

Isabel Praça; Sérgio Ramos; Rui Andrade; Allon Soares Da Silva; Everthon Taghori Sica. 2019. "Analysis and Simulation of Local Energy Markets." 2019 16th International Conference on the European Energy Market (EEM) , no. : 1.

Original research
Published: 07 August 2019 in Journal of Ambient Intelligence and Humanized Computing
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Cyber security is a critical area in computer systems especially when dealing with sensitive data. At present, it is becoming increasingly important to assure that computer systems are secured from attacks due to modern society dependence from those systems. To prevent these attacks, nowadays most organizations make use of anomaly-based intrusion detection systems (IDS). Usually, IDS contain machine learning algorithms which aid in predicting or detecting anomalous patterns in computer systems. Most of these algorithms are supervised techniques, which contain gaps in the detection of unknown patterns or zero-day exploits, since these are not present in the algorithm learning phase. To address this problem, we present in this paper an empirical study of several unsupervised learning algorithms used in the detection of unknown attacks. In this study we evaluated and compared the performance of different types of anomaly detection techniques in two public available datasets: the NSL-KDD and the ISCX. The aim of this evaluation allows us to understand the behavior of these techniques and understand how they could be fitted in an IDS to fill the mentioned flaw. Also, the present evaluation could be used in the future, as a comparison of results with other unsupervised algorithms applied in the cybersecurity field. The results obtained show that the techniques used are capable of carrying out anomaly detection with an acceptable performance and thus making them suitable candidates for future integration in intrusion detection tools.

ACS Style

Jorge Meira; Rui Andrade; Isabel Praça; João Carneiro; Verónica Bolón-Canedo; Amparo Alonso-Betanzos; Goreti Marreiros. Performance evaluation of unsupervised techniques in cyber-attack anomaly detection. Journal of Ambient Intelligence and Humanized Computing 2019, 11, 4477 -4489.

AMA Style

Jorge Meira, Rui Andrade, Isabel Praça, João Carneiro, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Goreti Marreiros. Performance evaluation of unsupervised techniques in cyber-attack anomaly detection. Journal of Ambient Intelligence and Humanized Computing. 2019; 11 (11):4477-4489.

Chicago/Turabian Style

Jorge Meira; Rui Andrade; Isabel Praça; João Carneiro; Verónica Bolón-Canedo; Amparo Alonso-Betanzos; Goreti Marreiros. 2019. "Performance evaluation of unsupervised techniques in cyber-attack anomaly detection." Journal of Ambient Intelligence and Humanized Computing 11, no. 11: 4477-4489.

Conference paper
Published: 25 June 2019 in Advances in Intelligent Systems and Computing
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The increase of renewable energy sources of intermittent nature has brought several new challenges for power and energy systems. In order to deal with the variability from the generation side, there is the need to balance it by managing consumption appropriately. Forecasting energy consumption becomes, therefore, more relevant than ever. This paper presents and compares three different ensemble learning methods, namely random forests, gradient boosted regression trees and Adaboost. Hour-ahead electricity load forecasts are presented for the building N of GECAD at ISEP campus. The performance of the forecasting models is assessed, and results show that the Adaboost model is superior to the other considered models for the one-hour ahead forecasts. The results of this study compared to previous works indicates that ensemble learning methods are a viable choice for short-term load forecast.

ACS Style

Jose Silva; Isabel Praça; Tiago Pinto; Zita Vale. Energy Consumption Forecasting Using Ensemble Learning Algorithms. Advances in Intelligent Systems and Computing 2019, 5 -13.

AMA Style

Jose Silva, Isabel Praça, Tiago Pinto, Zita Vale. Energy Consumption Forecasting Using Ensemble Learning Algorithms. Advances in Intelligent Systems and Computing. 2019; ():5-13.

Chicago/Turabian Style

Jose Silva; Isabel Praça; Tiago Pinto; Zita Vale. 2019. "Energy Consumption Forecasting Using Ensemble Learning Algorithms." Advances in Intelligent Systems and Computing , no. : 5-13.

Conference paper
Published: 22 June 2019 in Communications in Computer and Information Science
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This paper presents the application of collaborative reinforcement learning models to enable the distributed learning of energy contracts negotiation strategies. The learning model combines the learning process on the best negotiation strategies to apply against each opponent, in each context, from multiple learning sources. The diverse learning sources are the learning processes of several agents, which learn the same problem under different perspectives. By combining the different independent learning processes, it is possible to gather the diverse knowledge and reach a final decision on the most suitable negotiation strategy to be applied. The reinforcement learning process is based on the application of the Q-Learning algorithm; and the continuous combination of the different learning results applies and compares several collaborative learning algorithms, namely BEST-Q, Average (AVE)-Q; Particle Swarm Optimization (PSO)-Q, and Weighted Strategy Sharing (WSS)-Q. Results show that the collaborative learning process enables players’ to correctly identify the negotiation strategy to apply in each moment, context and against each opponent.

ACS Style

Tiago Pinto; Isabel Praça; Zita Vale; Carlos Santos. Collaborative Reinforcement Learning of Energy Contracts Negotiation Strategies. Communications in Computer and Information Science 2019, 202 -210.

AMA Style

Tiago Pinto, Isabel Praça, Zita Vale, Carlos Santos. Collaborative Reinforcement Learning of Energy Contracts Negotiation Strategies. Communications in Computer and Information Science. 2019; ():202-210.

Chicago/Turabian Style

Tiago Pinto; Isabel Praça; Zita Vale; Carlos Santos. 2019. "Collaborative Reinforcement Learning of Energy Contracts Negotiation Strategies." Communications in Computer and Information Science , no. : 202-210.

Conference paper
Published: 27 March 2019 in Advances in Intelligent Systems and Computing
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This paper proposes an adaptation of the Q-Learning reinforcement learning algorithm, for the identification of the most probable scenario that a player may face, under different contexts, when negotiating bilateral contracts. For that purpose, the proposed methodology is integrated in a Decision Support System that is capable to generate several different scenarios for each negotiation context. With this complement, the tool can also identify the most probable scenario for the identified negotiation context. A realistic case study is conducted, based on real contracts data, which confirms the learning capabilities of the proposed methodology. It is possible to identify the most probable scenario for each context over the learned period. Nonetheless, the identified scenario might not always be the real negotiation scenario, given the variable nature of such negotiations. However, this work greatly reduces the frequency of such unexpected scenarios, contributing to a greater success of the supported player over time.

ACS Style

Francisco Silva; Tiago Pinto; Isabel Praça; Zita Vale. Identifying Most Probable Negotiation Scenario in Bilateral Contracts with Reinforcement Learning. Advances in Intelligent Systems and Computing 2019, 556 -571.

AMA Style

Francisco Silva, Tiago Pinto, Isabel Praça, Zita Vale. Identifying Most Probable Negotiation Scenario in Bilateral Contracts with Reinforcement Learning. Advances in Intelligent Systems and Computing. 2019; ():556-571.

Chicago/Turabian Style

Francisco Silva; Tiago Pinto; Isabel Praça; Zita Vale. 2019. "Identifying Most Probable Negotiation Scenario in Bilateral Contracts with Reinforcement Learning." Advances in Intelligent Systems and Computing , no. : 556-571.