Characterization of threats in IoT from an MQTT protocol-oriented dataset

[EN] Nowadays, the cybersecurity of Internet of Thing (IoT) environments is a big challenge. The analysis of network traffic and the use of automated estimators built up with machine learning techniques have been useful in detecting intrusions in traditional networks. Since the IoT networks require new and particular protocols to control the communications between the different devices involved in the networks, the knowledge acquired in the study of general networks may be unuseful some times. The goal of this paper is twofold. On the one hand, we aim to obtain a consistent dataset of the network traffic of an IoT system based on the Message Queue Telemetry Transport protocol (MQTT) and undergoing certain type of attacks. On the other hand, we want to characterize each of these attacks in terms of the minimum possible number of significant variables allowed by this protocol. Obtaining the data set has been achieved by studying the MQTT protocol in depth, while its characterization has been addressed through a hybrid (filter/wrapper) feature selection algorithm based on the idea behind the minimum-redundancy maximum-relevance (mRMR) algorithm. The dataset, together with the feature selection algorithm, carries out a characterization of the different attacks which is optimal in terms of the accuracy of the machine learning models trained on it as well as in terms of the capability of explaining their underlying nature. This confirms the consistency of the datasetSIThis work is partially supported by Instituto Nacional de Ciberseguridad (INCIBE), Junta de Castilla y Leon–Consejería de Educación (LE078G18, UXXI2018/000149, U-220), powered by NVIDIA GPU Grant Program and developed in Research Institute of Applied Sciences in Cybersecurity (RIASC).Junta de Castilla y Leon–Consejería de Educación (LE078G18, UXXI2018/000149, U-220

Technical Audit of an Electronic Polling Station: A Case Study

P. 16-30This paper shows the lack of standard procedures to audit e-voting systems and also describes a practical process of auditing an e-voting experience based on a Direct-recording Electronic system (D.R.E). This system has been tested in a real situation, in the city council of Coahuila, Mexico, in November 2008. During the auditing, several things were kept in mind, in particular those critical in complex contexts, as democratic election processes are. The auditing process is divided into three main complementary stages: analysis of voting protocol, analysis of polling station hardware elements, and analysis of the software involved. Each stage contains several items which have to be analyzed at low level with the aim to detect and resolve possible security problemsS

An ontology-based approach to knowledge representation for Computer-Aided Control System Design

P. 107-125Different approaches have been used in order to represent and build control engineering concepts for the computer. Software applications for these fields are becoming more and more demanding each day, and new representation schemas are continuously being developed. This paper describes a study of the use of knowledge models represented in ontologies for building Computer Aided Control Systems Design (CACSD) tools. The use of this approach allows the construction of formal conceptual structures that can be stated independently of any software application and be used in many different ones. In order to show the advantages of this approach, an ontology and an application have been built for the domain of design of lead/lag controllers with the root locus method, presenting the results and benefits found

A Framework for the Optimization of Complex Cyber-Physical Systems via Directed Acyclic Graph

[EN] Mathematical modeling and data-driven methodologies are frequently required to optimize industrial processes in the context of Cyber-Physical Systems (CPS). This paper introduces the PipeGraph software library, an open-source python toolbox for easing the creation of machine learning models by using Directed Acyclic Graph (DAG)-like implementations that can be used for CPS. scikit-learn’s Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. It sequentially assembles several steps that can be cross-validated together while setting different parameters. Steps encapsulation secures the experiment from data leakage during the training phase. The scientific goal of PipeGraph is to extend the concept of Pipeline by using a graph structure that can handle scikit-learn’s objects in DAG layouts. It allows performing diverse operations, instead of only transformations, following the topological ordering of the steps in the graph; it provides access to all the data generated along the intermediate steps; and it is compatible with GridSearchCV function to tune the hyperparameters of the steps. It is also not limited to (�����,�����) entries. Moreover, it has been proposed as part of the scikit-learn-contrib supported project, and is fully compatible with scikit-learn. Documentation and unitary tests are publicly available together with the source code. Two case studies are analyzed in which PipeGraph proves to be essential in improving CPS modeling and optimization: the first is about the optimization of a heat exchange management system, and the second deals with the detection of anomalies in manufacturing processes.SIEspaña : Ministerio de Economía y Competitividad : grant number DPI2016-79960-C3-2-PMCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe

Rhetorical structure and persuasive language in the subgenre of online advertisements

p. 38-47This paper aims to reveal the rhetorical structure and the linguistic features of persuasive language in online advertisements of electronic products. Nowadays, the bulk of e-commerce is carried out in English, and it is often the case that non-native speakers are required to write different text types for various professional purposes, including promotional texts. This need has prompted the present study and the results have been used to build software to help native speakers of Spanish when writing promotional texts in English. The analysis reveals that these texts typically have two main rhetorical moves: one for identifying the product and another one for describing it. The latter move is further divided into two steps: one including objective features (size, weight, etc.) and the other focusing on persuading the potential customer. This is mainly achieved with the use of a relatively informal style (imperatives, contractions, clipping, subject/auxiliary omissions, etc.) and lexico-grammatical elements conveying positive evaluation (multiple modification, multal quantifying expressions, etc.). The findings show that online advertisements of electronic products may be regarded as a specific subgenre with particular macro- and microlinguistic characteristics, which have been identified in this paper for technical writing assistance.S

INTECO Webmessenger: Study of the Usability in Instant Messaging Systems

The main aim of this project is dual. Firstly, it is to achieve a complete analysis of the necessities of disabled people on the Internet. Secondly, it is intended to develop an instant messaging system based on the Windows Live Messenger whose requirements are defined over the previous analysis. This software is called INTECO Webmessenge

Multispecies bird sound recognition using a fully convolutional neural network

[EN] This study proposes a method based on fully convolutional neural networks (FCNs) to identify migratory birds from their songs, with the objective of recognizing which birds pass through certain areas and at what time. To determine the best FCN architecture, extensive experimentation was conducted through a grid search, exploring the optimal depth, width, and activation function of the network. The results showed that the optimal number of filters is 400 in the widest layer, with 4 convolutional blocks with maxpooling and an adaptive activation function. The proposed FCN offers a significant advantage over other techniques, as it can recognize the sound of a bird in audio of any length with an accuracy greater than 85%. Furthermore, due to its architecture, the network can detect more than one species from audio and can carry out near-real-time sound recognition. Additionally, the proposed method is lightweight, making it ideal for deployment and use in IoT devices. The study also presents a comparative analysis of the proposed method against other techniques, demonstrating an improvement of over 67% in the best-case scenario. These findings contribute to advancing the field of bird sound recognition and provide valuable insights into the practical application of FCNs in real-world scenarios.S

Improve Quality of Service for the Internet of Things using Blockchain & Machine Learning Algorithms.

[EN] The quality of service (QoS) parameters in IoT applications plays a prominent role in determining the performance of an application. Considering the significance and popularity of IoT systems, it can be predicted that the number of users and IoT devices are going to increase exponentially shortly. Therefore, it is extremely important to improve the QoS provided by IoT applications to increase their adaptability. Majority of the IoT systems are characterized by their heterogeneous and diverse nature. It is challenging for these systems to provide high-quality access to all the connecting devices with uninterrupted connectivity. Considering their heterogeneity, it is equally difficult to achieve better QoS parameters. Artificial intelligence-based machine learning (ML) tools are considered a potential tool for improving the QoS parameters in IoT applications. This research proposes a novel approach for enhancing QoS parameters in IoT using ML and Blockchain techniques. The IoT network with Blockchain technology is simulated using an NS2 simulator. Different QoS parameters such as delay, throughput, packet delivery ratio, and packet drop are analyzed. The obtained QoS values are classified using different ML models such as Naive Bayes (NB), Decision Tree (DT), and Ensemble, learning techniques. Results show that the Ensemble classifier achieves the highest classification accuracy of 83.74% compared to NB and DT classifiers.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Social network analysis for personalized characterization and risk assessment of alcohol use disorders in adolescents using semantic technologies

[EN] Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use of Social Network Analysis (SNA) techniques to study the different social networks (peers, friends, family, etc.) with whom the adolescent is involved. These kinds of studies need an initial phase of data gathering by means of questionnaires and a subsequent analysis phase using the SNA techniques. The process involves a number of manual data handling stages that are time consuming and error-prone. The use of knowledge engineering techniques (including the construction of a domain ontology) to represent the information, allows the automation of all the activities, from the initial data collection to the results of the SNA study. This paper shows how a knowledge model is constructed, and compares the results obtained using the traditional method with this, fully automated model, detailing the main advantages of the latter. In the case of the SNA analysis, the validity of the results obtained with the knowledge engineering approach are compared to those obtained manually using the UCINET, Cytoscape, Pajek and Gephi to test the accuracy of the knowledge model.S

Using an Ontology-based Approach to Build Open Assisting Tools in Foreign Language Writing

[EN] In today’s globalised world where there is a growing need for international communication, non-native speakers (NNS) from a wide range of professional fields are increasingly called upon to write specialised texts in English. More often than not, however, the linguistic competence required to do so is well beyond that of the majority of NNS. While software applications can serve to assist NNS in their English writing tasks, most of the applications available are designed for users of English for general purposes as opposed to English for professional purposes. Therefore, these applications lack the specific vocabulary, style guidelines and common structures required in more specialised documents. Necessary modifications to meet the needs of English for professional purposes tend to be viewed as representing an overly complex and expensive task. To overcome these challenges, we present a software called O-WEAA (Ontology-Writing English Assistant Architecture) which makes use of an ontology that represents the knowledge which, according to our formalisation, is required to write most types of specialised professional documents in the English language. Our formalisation of the required knowledge is based on an exhaustive linguistic analysis of several written genres. The proposed software is composed of two parts: i) a web application named Acquisition Interface Module, which allows experts to populate the ontology with new data and ii) a userfriendly, general web interface named Writing Assistant Interface Module which guides the user throughout the writing process of the English document in the specific domain described in the ontology.S