Development of security mechanisms for a multi-agent cyber-physical conveyor system using machine learning

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáOne main foundation of the Industry 4.0 is the connectivity of devices and systems using Internet of Things technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. CPS requires the use of Artificial Intelligence (AI) techniques, such as Multi-Agent Systems (MAS), allowing the incorporation of intelligence into the CPS through autonomous, proactive and cooperative entities. The adoption of this new generation of systems in the industrial environment opens new doors for various attacks that can cause serious damage to industrial production systems. This work presents the development of security mechanisms for systems based on MAS, where these mechanisms are used in an experimental case study that consists of a multiagent cyber-physical conveyor system. For this purpose, simple security mechanisms were employed in the system, such as user authentication, signature and message encryption, as well as other more complex mechanisms, such as machine learning techniques that allows the agents to be more intelligent in relation to the exchange of messages protecting the system against attacks, through the classification of the messages as reliable or not, and also an intrusion detection system was carried out. Based on the obtained results, the efficient protection of the system was reached, mitigating the main attack vectors present in the system architecture.Uma das principais bases da Indústria 4.0 é a conectividade de dispositivos e sistemas utilizando as tecnologias da Internet das Coisas, onde os sistemas ciber-físicos atuam como a infraestrutura principal com base em estruturas distribuídas e descentralizadas. Os sistemas ciber-físicos requerem o uso de técnicas de Inteligência Artificial, como por exemplo, Sistemas Multi-Agentes, permitindo a incorporação de inteligência nos sistemas ciber-físicos através de entidades autônomas, proativas e cooperativas. A adoção dessa nova geração de sistemas no ambiente industrial abre novas portas para vários ataques que podem causar sérios danos aos sistemas de produção industrial. Este trabalho apresenta o desenvolvimento de mecanismos de segurança para sistemas baseados em sistemas multi-agentes, em que esses mecanismos são utilizados em um caso de estudo experimental que consiste em um sistema de transporte ciber-físico baseado em sistemas multi-agentes. Para isso, mecanismos simples de segurança foram empregados no sistema, como autenticação do usuário, assinatura e criptografia de mensagens, além de outros mecanismos mais complexos, como técnicas de aprendizagem de máquina, que permite que os agentes sejam mais inteligentes em relação à troca de mensagens, protegendo o sistema contra ataques, através da classificação das mensagens como confiáveis ou não, e também foi realizado um sistema de detecção de intrusões. Com base nos resultados obtidos, obteve-se uma proteção eficiente do sistema, mitigando os principais vetores de ataque presentes na arquitetura do sistema

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.This work has received funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELK ARTEK programs. D. Camacho also acknowledges support from the Spanish Ministry of Science and Education under PID2020-117263GB-100 grant (FightDIS), the Comunidad Autonoma de Madrid under S2018/TCS-4566 grant (CYNAMON), and the CHIST ERA 2017 BDSI PACMEL Project (PCI2019-103623, Spain)

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical Analysis

Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS' technical aspects allows us to shed light on the ethical risks and challenges they might raise on individuals' well-being as they age. Moreover, the study proposes a categorization, understanding, and possible preventive/mitigation actions for the elicited risks and challenges through rethinking the AI ethics core principle of autonomy. Finally, elaborating on autonomy-related ethical theories, the paper proposes an autonomy-based ethical framework and how it can foster the development of autonomy-enabling HRS for AA

A novel smart energy management as a service over a cloud computing platform for nanogrid appliances

There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer’s knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Agile Processes in Software Engineering and Extreme Programming – Workshops

This open access book constitutes papers from the 5 research workshops, the poster presentations, as well as two panel discussions which were presented at XP 2021, the 22nd International Conference on Agile Software Development, which was held online during June 14-18, 2021. XP is the premier agile software development conference combining research and practice. It is a unique forum where agile researchers, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. XP conferences provide an informal environment to learn and trigger discussions and welcome both people new to agile and seasoned agile practitioners. The 18 papers included in this volume were carefully reviewed and selected from overall 37 submissions. They stem from the following workshops: 3rd International Workshop on Agile Transformation 9th International Workshop on Large-Scale Agile Development 1st International Workshop on Agile Sustainability 4th International Workshop on Software-Intensive Business 2nd International Workshop on Agility with Microservices Programmin

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)