SDIoT: A Software Defined based Internet of Things framework

The internet of things (IoT) represent the current and future state of the Internet. The large number of things (objects), which are connected to the Internet, produce a huge amount of data that needs a lot of effort and processing operations to transfer it to useful information. Moreover, the organization and control of this large volume of data requires novel ideas in the design and management of the IoT network to accelerate and enhance its performance. The software defined systems is a new paradigm that appeared recently to hide all complexity in traditional system architecture by abstracting all the controls and management operations from the underling devices (things in the IoT) and setting them inside a middleware layer, a software layer. In this work, a comprehensive software defined based framework model is proposed to simplify the IoT management process and provide a vital solution for the challenges in the traditional IoT architecture to forward, store, and secure the produced data from the IoT objects by integrating the software defined network, software defined storage, and software defined security into one software defined based control model

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Deploying AI Frameworks on Secure HPC Systems with Containers

The increasing interest in the usage of Artificial Intelligence techniques (AI) from the research community and industry to tackle "real world" problems, requires High Performance Computing (HPC) resources to efficiently compute and scale complex algorithms across thousands of nodes. Unfortunately, typical data scientists are not familiar with the unique requirements and characteristics of HPC environments. They usually develop their applications with high-level scripting languages or frameworks such as TensorFlow and the installation process often requires connection to external systems to download open source software during the build. HPC environments, on the other hand, are often based on closed source applications that incorporate parallel and distributed computing API's such as MPI and OpenMP, while users have restricted administrator privileges, and face security restrictions such as not allowing access to external systems. In this paper we discuss the issues associated with the deployment of AI frameworks in a secure HPC environment and how we successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.Comment: 6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing Conferenc

On the security of software-defined next-generation cellular networks

In the recent years, mobile cellular networks are ndergoing fundamental changes and many established concepts are being revisited. Future 5G network architectures will be designed to employ a wide range of new and emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV). These create new virtual network elements each affecting the logic of the network management and operation, enabling the creation of new generation services with substantially higher data rates and lower delays. However, new security challenges and threats are also introduced. Current Long-Term Evolution (LTE) networks are not able to accommodate these new trends in a secure and reliable way. At the same time, novel 5G systems have proffered invaluable opportunities of developing novel solutions for attack prevention, management, and recovery. In this paper, first we discuss the main security threats and possible attack vectors in cellular networks. Second, driven by the emerging next-generation cellular networks, we discuss the architectural and functional requirements to enable appropriate levels of security