A MEC-IIoT intelligent threat detector based on machine learning boosted tree algorithms

In recent years, new management methods have appeared that mark the beginning of a new industrial revolution called Industry 4.0 or the Industrial Internet of Things (IIoT). IIoT brings together new emerging technologies, such as the Internet of Things (IoT), Deep Learning (DL) and Machine Learning (ML), that contribute to new applications, industrial processes and efficiency management in factories. This combination of new technologies and contexts is paired with Multi-access Edge Computing (MEC) to reduce costs through the virtualisation of networks and services. As these new paradigms increase in growth, so does the number of threats and vulnerabilities, making IIoT a very desirable target for cybercriminals. In addition, IIoT devices have certain intrinsic limitations, especially due to their limited resources, and this makes it impossible, in many cases, to detect attacks by using solutions designed for other paradigms. So it is necessary to design, implement and evaluate new solutions or adapt existing ones. Therefore, this paper proposes an intelligent threat detector based on boosted tree algorithms. Such detectors have been implemented and evaluated in an environment specifically designed to test IIoT deployments. In this way, we can learn how these algorithms, which have been successful in multiple contexts, behave in a paradigm with known constraints. The results obtained in the study show that our intelligent threat detector achieves a mean efficiency of between 95%–99% in the F1 Score metric, indicating that it is a good option for implementation in these scenarios

MECInOT: a multi-access edge computing and industrial internet of things emulator for the modelling and study of cybersecurity threats

In recent years, the Industrial Internet of Things (IIoT) has grown rapidly, a fact that has led to an increase in the number of cyberattacks that target this environment and the technologies that it brings together. Unfortunately, when it comes to using tools for stopping such attacks, it can be noticed that there are inherent weaknesses in this paradigm, such as limitations in computational capacity, memory and network bandwidth. Under these circumstances, the solutions used until now in conventional scenarios cannot be directly adopted by the IIoT, and so it is necessary to develop and design new ones that can effectively tackle this problem. Furthermore, these new solutions must be tested in order to verify their performance and viability, which requires testing architectures that are compatible with newly introduced IIoT topologies. With the aim of addressing these issues, this work proposes MECInOT, which is an architecture based on openLEON and capable of generating test scenarios for the IIoT environment. The performance of this architecture is validated by creating an intelligent threat detector based on tree-based algorithms, such as decision tree, random forest and other machine learning techniques. Which allows us to generate an intelligent and to demonstrate, we could generate an intelligent threat detector and demonstrate the suitability of our architecture for testing solutions in IIoT environments. In addition, by using MECInOT, we compare the performance of the different machine learning algorithms in an IIoT network. Firstly, we present the benefits of our proposal, and secondly, we describe the emulation of an IIoT environment while ensuring the repeatability of the experiments

Impacto en el estado de resultados de una compañía porcícola en Colombia ante el uso de coberturas cambiarias para la adquisición de maíz

En Colombia las compañías del sector agropecuario son dependientes de insumos importados. Esto obliga a que las compañías realicen transacciones en divisas extranjeras, principalmente en dólares. Esta situación hace que las empresas tengan una exposición a riesgo cambiario, que, junto a la volatilidad del peso colombiano y su dependencia del dinamismo de la economía mundial, vuelve interesante la posibilidad de utilizar herramientas financieras, como derivados financieros que permitan minimizar o mitigar el riesgo inherente en esta actividad. Esta volatilidad en la tasa de cambio genera incertidumbre y alta variación en los flujos de caja de las compañías del sector agropecuario, por la dificultad de hacer una proyección acertada del flujo de caja, ya que hay un desafío importante en proyectar el valor que tendrá la divisa al momento de la importación. Así, el propósito de este documento es evaluar el impacto que tendría el uso de coberturas al riesgo cambiario para la adquisición de maíz en el estado de resultados de una compañía porcícola en Colombia.In Colombia, companies in the agricultural sector rely on imported materials. This forces companies to conduct transactions in foreign currencies, primarily in US dollars. This situation exposes companies to foreign exchange risk, which, along with the volatility of the Colombian peso and its dependence on the dynamism of the global economy, makes it interesting to consider the possibility of using financial instruments such as derivatives to minimize or mitigate the inherent risk in this activity. The volatility in the exchange rate generates uncertainty and high variation in the cash flows of companies in the agricultural sector due to the difficulty of making an accurate projection of cash flow, as there is a significant challenge in projecting the value that the currency will have at the time of importation. Thus, the purpose of this document is to evaluate the impact that the use of hedging through derivatives to manage exchange rate risk for the acquisition of corn would have on the income statement of a pork company in Colombia

Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures

The Internet of Things (IoT) paradigm keeps growing, and many different IoT devices, such as smartphones and smart appliances, are extensively used in smart industries and smart cities. The benefits of this paradigm are obvious, but these IoT environments have brought with them new challenges, such as detecting and combating cybersecurity attacks against cyber-physical systems. This paper addresses the real-time detection of security attacks in these IoT systems through the combined used of Machine Learning (ML) techniques and Complex Event Processing (CEP). In this regard, in the past we proposed an intelligent architecture that integrates ML with CEP, and which permits the definition of event patterns for the real-time detection of not only specific IoT security attacks, but also novel attacks that have not previously been defined. Our current concern, and the main objective of this paper, is to ensure that the architecture is not necessarily linked to specific vendor technologies and that it can be implemented with other vendor technologies while maintaining its correct functionality. We also set out to evaluate and compare the performance and benefits of alternative implementations. This is why the proposed architecture has been implemented by using technologies from different vendors: firstly, the Mule Enterprise Service Bus (ESB) together with the Esper CEP engine; and secondly, the WSO2 ESB with the Siddhi CEP engine. Both implementations have been tested in terms of performance and stress, and they are compared and discussed in this paper. The results obtained demonstrate that both implementations are suitable and effective, but also that there are notable differences between them: the Mule-based architecture is faster when the architecture makes use of two message broker topics and compares different types of events, while the WSO2-based one is faster when there is a single topic and one event type, and the system has a heavy workload.This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Union FEDER Funds [grant numbers FPU 17/02007, RTI2018-093608-B-C33, RTI2018-098156-B-C52 and RED2018-102654-T] . This work was also supported by the JCCM [grant number SB-PLY/17/180501/000353] and the Research Plan from the University of Cadiz and Grupo Energetico de Puerto Real S.A. under project GANGES [grant number IRTP03' UCA] . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Proyecto Carteia: primeros resultados

En el presente artículo presentamos los primeros resultados del proyecto de investigación "estudio histórico arqueológico de la ciudad púnico romana de Carteia" desarrollados durante las campañas de 1994 y 1995. En el se aborda, de forma general, el desarrollo histórico y urbanístico de la ciudad desde sus primeras etapas históricas hasta la edad media profundizando en el estudio del desarrollo urbanístico, de su arquitectura y de sus fases constructivas, así como del conjunto de sus materiales arqueológicos. Las actuaciones de estas dos primeras campañas se han centrado en tres sectores del yacimiento que corresponden a tres problemas arqueológicos distintos: el estudio de la secuencia estratigráfica desde los primeros niveles de habitación de la ciudad púnica intrapuesta al foro ; los análisis de la estructura del complejo monumental del templo y, por último, el estudio del periodo medieval

A Natural Alternative Treatment for Urinary Tract Infections: Itxasol©, the Importance of the Formulation

Genito-urinary tract infections have a high incidence in the general population, being more prevalent among women than men. These diseases are usually treated with antibiotics, but very frequently, they are recurrent and lead to the creation of resistance and are associated with increased morbidity and mortality. For this reason, it is necessary to develop new compounds for their treatment. In this work, our objective is to review the characteristics of the compounds of a new formulation called Itxasol© that is prescribed as an adjuvant for the treatment of UTIs and composed of β-arbutin, umbelliferon and n-acetyl cysteine. This formulation, based on biomimetic principles, makes Itxasol© a broad-spectrum antibiotic with bactericidal, bacteriostatic and antifungal properties that is capable of destroying the biofilm and stopping its formation. It also acts as an anti-inflammatory agent, without the adverse effects associated with the recurrent use of antibiotics that leads to renal nephrotoxicity and other side effects. All these characteristics make Itxasol© an ideal candidate for the treatment of UTIs since it behaves like an antibiotic and with better characteristics than other adjuvants, such as D-mannose and cranberry extracts

Recent advances in biomedical photonic sensors: a focus on optical-fibre-based sensing

In this invited review, we provide an overview of the recent advances in biomedical pho tonic sensors within the last five years. This review is focused on works using optical-fibre technology, employing diverse optical fibres, sensing techniques, and configurations applied in several medical fields. We identified technical innovations and advancements with increased implementations of optical-fibre sensors, multiparameter sensors, and control systems in real applications. Examples of outstanding optical-fibre sensor performances for physical and biochemical parameters are covered, including diverse sensing strategies and fibre-optical probes for integration into medical instruments such as catheters, needles, or endoscopes.This work was supported by Ministerio de Ciencia e Innovación and Agencia Estatal de Investigación (PID2019-107270RB-C21/AEI/10.13039/501100011033), and TeDFeS Project (RTC-2017- 6321-1) co-funded by European FEDER funds. M.O. and J.F.A. received funding from Ministerio de Ciencia, Innovación y Universidades of Spain under Juan de la Cierva-Formación and Juan de la Cierva-Incorporación grants, respectively. P.R-V. received funding from Ministerio de Educación, Cultura y Deporte of Spain under PhD grant FPU2018/02797

Light technology for efficient and effective photodynamic therapy: A critical review

Photodynamic therapy (PDT) is a cancer treatment with strong potential over well-established standard therapies in certain cases. Non-ionising radiation, localisation, possible repeated treatments, and stimulation of immunological response are some of the main beneficial features of PDT. Despite the great potential, its application remains challenging. Limited light penetration depth, non-ideal photosensitisers, complex dosimetry, and complicated implementations in the clinic are some limiting factors hindering the extended use of PDT. To surpass actual technological paradigms, radically new sources, light-based devices, advanced photosensitisers, measurement devices, and innovative application strategies are under extensive investigation. The main aim of this review is to highlight the advantages/pitfalls, technical challenges and opportunities of PDT, with a focus on technologies for light activation of photosensitisers, such as light sources, delivery devices, and systems. In this vein, a broad overview of the current status of superficial, interstitial, and deep PDT modalities - and a critical review of light sources and their effects on the PDT process - are presented. Insight into the technical advancements and remaining challenges of optical sources and light devices is provided from a physical and bioengineering perspective.This work was supported by Ministerio de Ciencia e Innovación and Agencia Estatal de Investigación (PID2019-107270RB-C21/AIE/10.13039/501100011033