A situational awareness model for data analysis on 5G mobile networks : the SELFNET analyzer framework

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 14-07-2017Se espera que las redes 5G provean un entorno seguro, con able y de alto rendimiento con interrupciones m nimas en la provisi on de servicios avanzados de red, sin importar la localizaci on del dispositivo o cuando el servicio es requerido. Esta nueva generaci on de red ser a capaz de proporcionar altas velocidades, baja latencia y mejor Calidad de Servicio (QoS) comparado con las redes actuales Long Term Evolution (LTE). Para proveer estas capacidades, 5G propone la combinaci on de tecnolog as avanzadas tales como Redes De nidas por Software (SDN), Virtualizaci on de las Funciones de Red (NFV), Redes auto-organizadas (SON) e Inteligencia Arti cial. De manera especial, 5G ser a capaz de solucionar o mitigar cambios inesperados o problemas t picos de red a trav es de la identi caci on de situaciones espec cas, tomando en cuenta las necesidades del usuario y los Acuerdos de Nivel de Servicio (SLAs). Actualmente, los principales operadores de red y la comunidad cient ca se encuentran trabajando en estrategias para facilitar el an alisis de datos y el proceso de toma de decisiones cuando eventos espec cos comprometen la salud de las redes 5G. Al mismo tiempo, el concepto de Conciencia Situacional (SA) y los modelos de gesti on de incidencias aplicados a redes 5G est an en etapa temprana de desarrollo. La idea principal detr as de estos conceptos es prevenir o mitigar situaciones nocivas de manera reactiva y proactiva. En este contexto, el proyecto Self-Organized Network Management in Virtualized and Software De ned Networks (SELFNET) combina los conceptos de SDN, NFV and SON para proveer un marco de gesti on aut onomo e inteligente para redes 5G. SELFNET resuelve problemas comunes de red, mientras mejora la calidad de servicio (QoS) y la Calidad de Experiencia (QoE) de los usuarios nales...5G networks hope to provide a secure, reliable and high-performance environment with minimal disruptions in the provisioning of advanced network services, regardless the device location or when the service is required. This new network generation will be able to deliver ultra-high capacity, low latency and better Quality of Service (QoS) compared with current Long Term Evolution (LTE) networks. In order to provide these capabilities, 5G proposes the combination of advanced technologies such as Software De ned Networking (SDN), Network Function Virtualization (NFV), Self-organized Networks (SON) or Arti cial Intelligence. In particular, 5G will be able to face unexpected changes or network problems through the identi cation of speci c situations, taking into account the user needs and the Service Level Agreements (SLAs). Nowadays, the main telecommunication operators and community research are working in strategies to facilitate the data analysis and decision-making process when unexpected events compromise the health in 5G Networks. Meanwhile, the concept of Situational Awareness (SA) and incident management models applied to 5G Networks are also in an early stage. The key idea behind these concepts is to mitigate or prevent harmful situations in a reactive and proactive way. In this context, Self-Organized Network Management in Virtualized and Software De ned Networks Project (SELFNET) combines SDN, NFV and SON concepts to provide a smart autonomic management framework for 5G networks. SELFNET resolves common network problems, while improving the QoS and Quality of Experience (QoE) of end users...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

An Approach to Data Analysis in 5G Networks

5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEUnión Europea. Horizonte 2020pu

Trends on Computer Security: Cryptography, User Authentication, Denial of Service and Intrusion Detection

The new generation of security threats has beenpromoted by digital currencies and real-time applications, whereall users develop new ways to communicate on the Internet.Security has evolved in the need of privacy and anonymity forall users and his portable devices. New technologies in everyfield prove that users need security features integrated into theircommunication applications, parallel systems for mobile devices,internet, and identity management. This review presents the keyconcepts of the main areas in computer security and how it hasevolved in the last years. This work focuses on cryptography,user authentication, denial of service attacks, intrusion detectionand firewalls

Profits at the dawn of cybercrime-as-a-service

The growing of Information and Communication Technologies (ICT) that has been experienced in recent years, has led to new and more sophisticated ways of doing business. Consequently, worldwide organized criminal groups have been able to adapt their activities to new trends in the area of information security. In this paper the problem of cyber-crime as a profitable business and the model Cybercrime-as-a-service (CaaS) are exposed. For this purpose, the ransomware, which is one of the threats that have generated more profit in the last two years, is analyzed. This kind of malware is able to block assets in the victim systems and blackmail their owners with their deletion, if they fail to pay a ransom. In this sense, a game theory model of the behavior of actors involved in a ransomware attack is proposed. The proposed model describes the extortion process between the attacker and victim and estimates the probability of payment of ransom

Comparativa del avance en desarrollo en las telecomunicaciones entre Ecuador y Bolivia.

El sector de las telecomunicaciones es uno de los principales motores para el desarrollo de un país. Un alto nivel de conectividad facilita el desarrollo de actividades productivas mejorando la calidad de vida de las personas. En el presente artículo se analiza la situación de las telecomunicaciones entre Ecuador y Bolivia. El estudio incluye un análisis de la situación legal y avance tecnológico del sector de las telecomunicaciones de cada país. Además, el trabajo presenta comparativas de los parámetros más relevantes tales como tarifas, líneas fijas, móviles, interconexión, entre otros

Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks

Hand gesture recognition systems (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) have been studied for different applications in recent years. Most commonly, cutting-edge HGR methods are based on supervised machine learning methods. However, the potential benefits of reinforcement learning (RL) techniques have shown that these techniques could be a viable option for classifying EMGs. Methods based on RL have several advantages such as promising classification performance and online learning from experience. In this work, we developed an HGR system made up of the following stages: pre-processing, feature extraction, classification, and post-processing. For the classification stage, we built an RL-based agent capable of learning to classify and recognize eleven hand gestures—five static and six dynamic—using a deep Q-network (DQN) algorithm based on EMG and IMU information. The proposed system uses a feed-forward artificial neural network (ANN) for the representation of the agent policy. We carried out the same experiments with two different types of sensors to compare their performance, which are the Myo armband sensor and the G-force sensor. We performed experiments using training, validation, and test set distributions, and the results were evaluated for user-specific HGR models. The final accuracy results demonstrated that the best model was able to reach up to 97.50%±1.13% and 88.15%±2.84% for the classification and recognition, respectively, with regard to static gestures, and 98.95%±0.62% and 90.47%±4.57% for the classification and recognition, respectively, with regard to dynamic gestures with the Myo armband sensor. The results obtained in this work demonstrated that RL methods such as the DQN are capable of learning a policy from online experience to classify and recognize static and dynamic gestures using EMG and IMU signals

Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks

In recent years, hand gesture recognition (HGR) technologies that use electromyography (EMG) signals have been of considerable interest in developing human–machine interfaces. Most state-of-the-art HGR approaches are based mainly on supervised machine learning (ML). However, the use of reinforcement learning (RL) techniques to classify EMGs is still a new and open research topic. Methods based on RL have some advantages such as promising classification performance and online learning from the user’s experience. In this work, we propose a user-specific HGR system based on an RL-based agent that learns to characterize EMG signals from five different hand gestures using Deep Q-network (DQN) and Double-Deep Q-Network (Double-DQN) algorithms. Both methods use a feed-forward artificial neural network (ANN) for the representation of the agent policy. We also performed additional tests by adding a long–short-term memory (LSTM) layer to the ANN to analyze and compare its performance. We performed experiments using training, validation, and test sets from our public dataset, EMG-EPN-612. The final accuracy results demonstrate that the best model was DQN without LSTM, obtaining classification and recognition accuracies of up to 90.37%±10.7% and 82.52%±10.9%, respectively. The results obtained in this work demonstrate that RL methods such as DQN and Double-DQN can obtain promising results for classification and recognition problems based on EMG signals