Alberto Huertas Celdrán

Design of a recommender system based on users’ behavior and collaborative location and tracking

Abstract During the last years, mobile devices allow incorporating users’ location and movements into recommendations to potentially suggest most valuable information. In this context, this paper presents a hybrid recommender algorithm that combines users’ location and preferences and the content of the items located close to such users. This algorithm also includes a way of providing implicit ratings considering the users’ movements after receiving recommendations, aimed at measuring the users’ interest for the recommended items. Conducted experiments measure the effectiveness and the efficiency of our recommender algorithm, as well as the impact of implicit ratings.

Dynamic Reconfiguration in 5G Mobile Networks to Proactively Detect and Mitigate Botnets

Botnets are one of the most powerful cyberthreats affecting continuity and delivery of existing network services. Detecting and mitigating attacks promoted by botnets become a greater challenge with the advent of 5G networks, as the number of connected devices with high mobility capabilities, the volume of exchange data, and the transmission rates increase significantly. Here, a 5G-oriented solution is proposed for proactively detecting and mitigating botnets in a highly dynamic 5G network. 5G subscribers’ mobility requires dynamic network reconfiguration, which is handled by combining software-defined network and network function virtualization techniques.

Automatic monitoring management for 5G mobile networks

Abstract 5G mobile networks are pushing new dynamic and flexible scenarios that require the automation of the management processes performed by network administrators. To this end, Self-Organizing Networks (SON) arose with the goal of moving from traditional manual management processes towards an automatic and dynamic perspective. The orchestration of the monitoring services is an essential task to conduct self-configuration, self-healing, and self-optimization processes required by SONs. In this context, we propose a solution that efficiently orchestrates the monitoring services by managing the network resources automatically. In particular, we propose a 5G-oriented architecture that integrates the Software Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies to monitor and orchestrate the whole life-cycle of monitoring services considering information of the network control plane.

Dynamic management of a deep learning-based anomaly detection system for 5G networks

Fog and mobile edge computing (MEC) will play a key role in the upcoming fifth generation (5G) mobile networks to support decentralized applications, data analytics and management into the network itself by using a highly distributed compute model. Furthermore, increasing attention is paid to providing user-centric cybersecurity solutions, which particularly require collecting, processing and analyzing significantly large amount of data traffic and huge number of network connections in 5G networks. In this regard, this paper proposes a MEC-oriented solution in 5G mobile networks to detect network anomalies in real-time and in autonomic way. Our proposal uses deep learning techniques to analyze network flows and to detect network anomalies. Moreover, it uses policies in order to provide an efficient and dynamic management system of the computing resources used in the anomaly detection process. The paper presents relevant aspects of the deployment of the proposal and experimental results to show its performance.

Towards the autonomous provision of self-protection capabilities in 5G networks

Abstract5G mobile networks are pushing new dynamic and flexible scenarios that demand the automation and optimization of network management processes. In this sense, Self-Organizing Networks (SON) arose to evolve from traditional manual management towards fully autonomic and dynamic processes. Due to the large volumes of data generated in 5G networks, functionalities and capabilities of SON require efficient processes and resource optimization techniques. In particular, self-protection is a critical capability of SON focused on protecting the network resources in a flexible and autonomic way. To achieve self-protection, SON perform different processes ranging from the monitoring of network communications to the analysis, detection, and mitigation of cyber-attacks. In this article, we propose an architecture that combines the Software Defined Networking and Network Functions Virtualization technologies to optimize the usage of network resources for monitoring services. A use case based on botnet detection in 5G networks shows how our architecture ensures the provision of monitoring services in managing self-protection scenarios. Additionally, we describe a set of experiments that confirm the best time calculated by our solution to deploy or reconfigure monitoring and detection services. These experiments consider different aspects like the number of zombies shaping the botnet, their mobility, or network traffic.

Preserving patients’ privacy in health scenarios through a multicontext-aware system

The Big Data age is characterized by the explosive increase of data managed by electronic systems. Healthcare Information Management systems are aware of this situation having to adapt services and procedures. This, along with the fact that the proliferation of mobile devices and communications has also promoted the use of context-aware services ubiquitously accessible, means that protecting the privacy of the patients’ information is an even greater challenge. To address this issue, a mechanism that allows patients to manage and control their private information is required. We propose the preservation of patients’ privacy in a health scenario through a multicontext-aware system called h-MAS (health-related multicontext-aware system). h-MAS is a privacy-preserving and context-aware solution for health scenarios with the aim of managing the privacy of the users’ information in both intra- and inter-context scenarios. In a health scenario, h-MAS suggests a pool of privacy policies to users, who are aware of the health context in which they are located. Users can update the policies according to their interests. These policies protect the privacy of the users’ health records, locations, as well as context-aware information being accessed by third parties without their consent. The information on patients and the health context is managed through semantic web techniques, which provide a common infrastructure that makes it possible to represent, process, and share information between independent systems more easily.The Big Data age is characterized by the explosive increase of data managed by electronic systems. Healthcare Information Management systems are aware of this situation having to adapt services and procedures. This, along with the fact that the proliferation of mobile devices and communications has also promoted the use of context-aware services ubiquitously accessible, means that protecting the privacy of the patients’ information is an even greater challenge. To address this issue, a mechanism that allows patients to manage and control their private information is required. We propose the preservation of patients’ privacy in a health scenario through a multicontext-aware system called h-MAS (health-related multicontext-aware system). h-MAS is a privacy-preserving and context-aware solution for health scenarios with the aim of managing the privacy of the users’ information in both intra- and inter-context scenarios. In a health scenario, h-MAS suggests a pool of privacy policies to users, who are aware of the health context in which they are located. Users can update the policies according to their interests. These policies protect the privacy of the users’ health records, locations, as well as context-aware information being accessed by third parties without their consent. The information on patients and the health context is managed through semantic web techniques, which provide a common infrastructure that makes it possible to represent, process, and share information between independent systems more easily.

Policy-Based Management for Green Mobile Networks Through Software-Defined Networking

Traditional networks are characterized by wasting considerable amount of energy that could be reduced drastically. The challenge of energy saving should be managed efficiently, where the mobility of users and services are nominated to play a significant role as well as the use of the Software Defined Networking (SDN) paradigm. Besides the network management supported by the SDN paradigm, we highlight the management of the network infrastructure at run-time, considering aspects like the energy efficiency. In this paper, we present an energy-aware and policy-based system oriented to the SDN paradigm, which allows managing the network infrastructure dynamically at run-time and on demand through policies. With these policies, any network using our solution will be able to reduce energy consumption by switching on/off its resources when they are inefficient, and creating virtualized network resources like proxies to reduce the network traffic. The experiments conducted demonstrate how the energy consumption is reduced when enforcing the proposed policies, considering aspects such as the number of base stations, their cell sizes, and the number of active devices in a given time, among other.