The exponential growth of devices connected to the network has resulted in the development of new IoT applications and on-line services. However, these advances are limited by the rigidity of the current network infrastructure, in which the administrator has to implement high-level network policies adapting and configuring protocols manually and usually through a command line interface (CLI). At this point, Software-Defined Networking (SDN) appears as a viable alternative network architecture that allows for programming the network and opening the possibility of creating new services and more efficient applications to cover the actual requirements. In this paper, we describe this new technology and analyze its opportunities in the development of IoT applications. Similarly, we present the first applications and projects based on this technology. Finally, we discuss the issues and challenges in its implementation.

The concept of Future Networks is based on the premise that current infrastructures require enhanced control, service customization, self-organization and self-management capabilities to meet the new needs in a connected society, especially of mobile users. In order to provide a high-performance mobile system, three main fields must be improved: radio, network, and operation and management. In particular, operation and management capabilities are intended to enable business agility and operational sustainability, where the addition of new services does not imply an excessive increase in capital or operational expenditures. In this context, a set of key-enabled technologies have emerged in order to aid in this field. Concepts such as Software Defined Network (SDN), Network Function Virtualization (NFV) and Self-Organized Networks (SON) are pushing traditional systems towards the next 5G network generation.This paper presents an overview of the current status of these promising technologies and ongoing works to fulfill the operational and management requirements of mobile infrastructures. This work also details the use cases and the challenges, taking into account not only SDN, NFV, cloud computing and SON but also other paradigms.

In recent years, cybercrime activities have grown significantly, compromising device security and jeopardizing the normal activities of enterprises. The profits obtained through intimidation and the limitations for tracking down the illegal transactions have created a lucrative business based on the hijacking of users’ files. In this context, ransomware takes advantage of cryptography to compromise the user information or deny access to the operating system. Then, the attacker extorts the victim to pay a ransom in order to regain access, recover the data, or keep the information private. Nowadays, the adoption of Situational Awareness (SA) and cognitive approaches can facilitate the rapid identification of ransomware threats. SA allows knowing what is happening in compromised devices and network communications through monitoring, aggregation, correlation, and analysis tasks. The current literature provides some parameters that are monitored and analyzed in order to prevent these kinds of attacks at an early stage. However, there is no complete list of them. To the best of our knowledge, this paper is the first proposal that summarizes the parameters evaluated in this research field and considers the SA concept. Furthermore, there are several articles that tackle ransomware problems. However, there are few surveys that summarize the current situation in the area, not only regarding its evolution but also its issues and future challenges. This survey also provides a classification of ransomware articles based on detection and prevention approaches.

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.

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The fifth generation mobile network, or 5G, moves towards bringing solutions to deploying faster networks, with hundreds of thousands of simultaneous connections and massive data transfer. For this purpose, several emerging technologies are implemented, resulting in virtualization and self-organization of most of their components, which raises important challenges related to safety. In order to contribute to their resolution, this paper proposes a novel architecture for incident management on 5G. The approach combines the conventional risk management schemes with the Endsley Situational Awareness model, thus improving effectiveness in different aspects, among them the ability to adapt to complex and dynamical monitoring environments, and countermeasure tracking or the role of context when decision-making. The proposal takes into account all layers for information processing in 5G mobile networks, ranging from infrastructure to the actuators responsible for deploying corrective measures.

Software defined networking (SDN) and network function virtualisation (NFV) have become hot topics in recent years. On one hand, SDN decouples the control plane from the data plane allowing the rapid innovation and the introduction of new services in an easy way. On the other hand, currently proprietary appliances such as load balancers and firewalls are implemented in hardware, NFV aims to change these network functions to an open software environment using virtualisation and cloud technologies. This means a reduction of spends in the provisioning and management of telecom services. SDN and NFV are two different concepts but these can coexist and help each other. In this study, the authors present a survey of SDN and NFV focusing in virtualisation projects and the use cases where a synergy between these technologies is possible. This study includes the basic concepts of network virtualisation, NFV and SDN, current research and the relation between both technologies.

Human trafficking is a global problem that strips away the dignity of millions of victims. Currently, social networks are used to spread this crime through the online environment by using covert messages that serve to promote these illegal services. In this context, since law enforcement resources are limited, it is vital to automatically detect messages that may be related to this crime and could also serve as clues. In this paper, we identify Twitter messages that could promote these illegal services and exploit minors by using natural language processing. The images and the URLs found in suspicious messages were processed and classified by gender and age group, so it is possible to detect photographs of people under 14 years of age. The method that we used is as follows. First, tweets with hashtags related to minors are mined in real-time. These tweets are preprocessed to eliminate noise and misspelled words, and then the tweets are classified as suspicious or not. Moreover, geometric features of the face and torso are selected using Haar models. By applying Support Vector Machine (SVM) and Convolutional Neural Network (CNN), we are able to recognize gender and age group, taking into account torso information and its proportional relationship with the head, or even when the face details are blurred. As a result, using the SVM model with only torso features has a higher performance than CNN.INDEX TERMS CNN, features detection, image classification, natural language processing, SVM.