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

Security for a multi-agent cyber-physical conveyor system using machine learning

One main foundation of Industry 4.0 is the connectivity of devices and systems using Internet of Things (IoT) technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. This approach provides significant benefits, namely improved performance, responsiveness and reconfigurability, but also brings some problems in terms of security, as the devices and systems become vulnerable to cyberattacks. This paper describes the implementation of several mechanisms to increase the security in a self-organized cyber-physical conveyor system, based on multi-agent systems (MAS) and build up with different individual modular and intelligent conveyor modules. For this purpose, the JADE-S add-on is used to enforce more security controls, also an Intrusion Detection System (IDS) is created supported by Machine Learning (ML) techniques that analyses the communication between agents, enabling to monitor and analyse the events that occur in the system, extracting signs of intrusions, together they contribute to mitigate cyberattacks.info:eu-repo/semantics/publishedVersio

New limits on WRW_R from meson decays

In this letter we show that pseudoscalar meson leptonic decay data can be used to set stringent limits on the mass $m_{W_R}$ of a right-handed vector boson, such as the one that appears in left-right symmetric models. We have shown that for a heavy neutrino with a mass $m_N$ in the range $50<m_N/{\rm MeV} <1900$ one can constraint $m_{W_R} \lesssim (4-19)$ TeV at 90 % CL. This provides the most stringent experimental limits on the $W_R$ mass to date.Comment: 6 pages, 2 figure

Self-organized cyber-physical conveyor system using multi-agent systems

The adoption of industrial cyber-physical systems is facing several challenges, with artificial intelligence and self-organization techniques assuming critical aspects to be considered in the deployment of such solutions to support the dynamic evolution and adaptation to condition changes. This paper describes the implementation of a modular, flexible and self-organized cyber-physical conveyor system build up with different individual modular and intelligent transfer modules. For this purpose, multi-agent systems are used to distribute intelligence among transfer modules supporting pluggability and modularity, complemented with self-organization capabilities to achieve a truly self-reconfigurable system. Furthermore, Internet of Things and Artificial Intelligence technologies are used to enable the real-time monitoring of the system, aiming the detection and prevention of anomalies in advance, and to enable the protection from possible external threats.info:eu-repo/semantics/publishedVersio

Learning cybersecurity in iot-based applications through a capture the flag competition

The Internet of Things (IoT) is one of the main foundations of Industry 4.0, providing widespread connectivity of systems and devices, which promotes significant benefits, such as improved performance, responsiveness, and reconfigurability. However, it also brings some security problems, which make these devices and systems vulnerable to cyberattacks, consequently demanding efficient learning and training initiatives to address the challenges regarding the qualification of undergraduate students and active professionals to design more secure systems, as well as to be more aware of cyberthreats during the management and use of them. With this in mind, this paper describes a Capture the Flag competition based on IoT cybersecurity. The participants’ feedback and performance evaluation show that this type of hands-on competition strongly contributes to learning the importance of cybersecurity in IoT-based applications.info:eu-repo/semantics/publishedVersio

IoT-based solution to reduce waste and promote a sustainable farming industry

Waste and the necessity to increase sustainability in the farming industry are some of the challenges addressed in the agri-food chain. With the potential of digital technologies, e.g., the Internet of Things (IoT) and Artificial Intelligence, to revolutionize agriculture by enabling more efficient and intelligent monitoring, system architecture and IoT nodes were developed to support relevant parameters for composing a Sustainability Index for the Bio-economy (siBIO). These nodes are scalable, modular, capable of meeting on-demand production needs, and provide a cost-effective alternative to commercial solutions or manual data collection methods. The collected data is transmitted to middleware and then stored, analyzed, and displayed on a user-friendly dashboard, providing data to siBIO and consequently contributing to a more sustainable farming industry and reducing waste of resources and food. The results include the implementation of IoT nodes in a case study involving a vineyard and an apple orchard. The nodes are successfully collecting data on environmental, operational, and energy parameters such as temperature, air humidity, soil moisture, precipitation, and water and electricity consumption for irrigation. The tests of data transmission and collection, functionality and robustness of the proposed solution were promising, offering a way to quantify the sustainability index and facilitate the exchange of agricultural information in a reliable and standardized way.This work has been conducted under the project BIOMA Bioeconomy integrated solutions for the mobilization of the Agro-food market (POCI-01-0247-FEDER-046112). This work has been also supported by the Foundation for Sci- ence and Technology (FCT, Portugal) through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Authors Gustavo Funchal and Vict oria Melo thank the FCT for the PhD Grants 2022.13712.BD and 2022.13868.BD, respectively.info:eu-repo/semantics/publishedVersio

Organic and conventional yerba mate (Ilex paraguariensis A. St. Hil) improves metabolic redox status of liver and serum in Wistar rats

Organic and conventional yerba mate (Ilex paraguariensis) is widely used in South America to prepare nonalcoholic drinks rich in polyphenols. These compounds are able to prevent the generation of reactive species, thus minimizing the incidence of several diseases. In this perspective, we hypothesized that yerba mate may have protective effects against pentylenetetrazol (PTZ)-induced oxidative damage in liver and serum of rats. Animals (n = 42) received distilled water (control) or yerba mate (organic or conventional) for fifteen days. Then, half of the rats of each group received 60 mg/kg PTZ intraperitoneally or saline solution. After 30 min the animals were euthanized and the liver and blood were collected. The results showed that organic and conventional yerba mate avoided PTZ-induced oxidative damage and nitric oxide production in the liver and serum of the rats. Moreover, both kinds of yerba mate prevented the decrease in enzymatic (superoxide dismutase and catalase) and non-enzymatic (sulfhydryl protein content) defenses in the liver and serum. In addition, histopathologic analysis of the liver showed that yerba mate reduced PTZ-induced cell damage. These findings indicate that yerba mate provides hepatoprotection and improves antioxidant status in the serum, which may contribute to the development of new therapeutic strategies using nutraceuticals drinks

Cloud-enabled integration of IoT applications within the farm to fork to reduce the food waste

The climate change, the loss of productive land area and the exponential population growth are demanding new and more efficient ways to produce, transform and consume food resources that respect the planet's sustainability. The achievement of these challenges require the use of emerging ICT technologies, such as Internet of Things (IoT), Artificial Intelligence (AI) and Cloud Computing, to promote the real-time information exchange along the farm to fork value chain, i.e. from the primary production to the food consumption, as well as to develop smart applications that take advantage of integrated information, e.g., aiming the process monitoring, prediction, optimization and traceability. This paper presents the development of a digital cloud-based ecosystem, built upon the open-source FIWARE platform, to enable the integration of smart applications and assets along the farm to fork chain, aiming to reduce the food waste. Furthermore, the feasibility of the proposed system is analysed through the integration of several IoT sensing nodes and a smart monitoring application, showing its benefits in terms of modularity, interoperability, scalability and robustness.info:eu-repo/semantics/publishedVersio

A fuzzy logic approach for self-managing energy efficiency in IoT nodes

The collection and analysis of data assume a crucial importance in the digital transformation era. Internet of Things (IoT) technologies allow to gather data from heterogeneous sources and make them available for data-driven systems aiming, e.g., monitoring, diagnosis, prediction and optimization. Several applications require that these IoT nodes be located remotely without connection to the electrical grid and being powered by batteries or renewable sources, thus requiring a more efficient management of the energy consumption in their operation. This paper aims to study and develop intelligent IoT nodes that embed Artificial Intelligence techniques to optimize their operation in terms of energy consumption when operating in constrained environments and powered by energy harvesting systems. For this purpose, a Fuzzy Logic system is proposed to determine the optimal operation strategy, considering the node’s current resource demands, the current battery condition and the power charge expectation. The proposed approach was implemented in IoT nodes measuring environmental parameters and placed in a university campus with Wi-Fi coverage. The achieved results show the advantage of adjusting the operation mode taking into consideration the battery level and the weather forecasts to increase the energy efficiency without compromising the IoT nodes’ functionalities and QoS.info:eu-repo/semantics/publishedVersio