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Given the increased growing of Internet of Things networks and their presence in critical aspects of human activities, the security of devices connected to these networks becomes critical. Machine Learning approaches are becoming prominent as enablers for security solutions in computer networks due to its capacity to process traffic information in order to detect abnormal patterns which might represent attacks targeting infrastructures. In this paper, we propose to leverage Convolutional and Recurrent Neural Networks, two artifacts that have been successfully used in contexts such as image processing for pattern recognition, for the development of a security solution to be used in the context of Internet of Things. Our results show that this approach, when evaluated with a state-of-the-art data set, achieves around 99% of accuracy in the binary classification of attacks (i.e. normal traffic vs attack traffic) and 96% for multiclass classification (recognition of different types of attacks) accuracy. These results outperform proposals available in literature, showing a promising landscape for developing security solutions for IoT infrastructures., Comment: 6 pages, 3 figures

Cloud applications have reshaped the model of services and infrastructure of the Internet. Search engines, social networks, content delivery and retail and e-commerce sites belong to this group of applications. An important element in the architecture of data centers where these applications run is the communication infrastructure, commonly known as data center networks (DCNs). A critical challenge DCNs have to address is the processing of the traffic of cloud applications, which due to its properties is essentially different to the traffic of other Internet applications. In order to improve the responsiveness and throughput of applications, DCNs should be able to prioritize short flows (a few KB) over long flows (several MB). However, given the time and space variations the traffic presents, the information about flow sizes is not available in advance in order to plan the flow scheduling. In this paper, we present an adaptable mechanism called Adaptable Workload-Agnostic Flow Scheduling (AWAFS). It is an adaptable approach that can adjust in an agnostic way the scheduling configuration of DCN forwarding devices. This agnostic adjustment contributes to reduce the Flow Completion Time (FCT) of those short flows, representing around 85% of the traffic handled by cloud applications. Our evaluation results based on simulation show that AWAFS can reduce the average FCT of short flows between 16.9% and 45.2% when compared to the best existing agnostic non-adaptable solution, without inducing starvation on long flows. Indeed, it can provide improvements as high as 39% for long flows. Additionally, AWAFS can improve the FCT for short flows in scenarios with high heterogeneity in the traffic present in the network, with a reduction up to 5% for the average FCT and 15% for the tail FCT., Comment: 21 pages, 29 figures

The advent of Programmable Data Planes represents an outstanding evolution and complete revolution of the Software- Defined Networking paradigm. The capacity to define the entire behavior of forwarding devices by controlling the packet parsing procedures and executing custom operations enables offloading functionalities traditionally performed at the control plane. A recent research line has explored the possibility of even offloading to the data plane part of Artificial Intelligence algorithms, and more specifically, Machine Learning ones, to increase their accuracy and responsiveness (by having more detailed visibility of the traffic). This introduces a significant opportunity for evolution in the critical field of Intrusion Detection. However, offloading functionalities to the data plane is not a straightforward task. In this paper, we discuss how Programmable Data Planes might complement different stages of an Intrusion Detection System based on Machine Learning. We present two use cases that make evident the feasibility of this approach and highlight aspects that must be considered when addressing the challenge of deploying solutions leveraging data-plane functionalities., Comment: 7 pages, 3 figures

In this article, a revision of literature on application of expert systems to fault management is presented. Then, a new approach, based on the autonomic computing paradigm and mobile agents technology is introduced. The main features of this new approach are compared to equivalent ones of expert systems. Benefits of the schema based on mobile agents are presented.

Este artículo presenta un análisis comparativo de tres aproximaciones que se pueden emplear para acceder a recursos del sistema bajo el sistema operativo Linux. Luego de una revisión de las principales características de cada aproximación, se realiza un experimento donde el comportamiento de estos es evaluado. Finalmente, los resultados son comparados y analizados.

Este artículo presenta un análisis comparativo de tres aproximaciones que se pueden emplear para acceder a recursos del sistema bajo el sistema operativo Linux. Luego de una revisión de las principales características de cada aproximación, se realiza un experimento donde el comportamiento de estos es evaluado. Finalmente, los resultados son comparados y analizados.

In this article, a revision of literature on application of expert systems to fault management is presented. Then, a new approach, based on the autonomic computing paradigm and mobile agents technology is introduced. The main features of this new approach are compared to equivalent ones of expert systems. Benefits of the schema based on mobile agents are presented.

This paper presents the most notable trends in the field of Fault Management, as an important issue in the evolution of service platforoms, basically in Telco Industry, because of the criticity of services running on them.

This paper presents the most notable trends in the field of Fault Management, as an important issue in the evolution of service platforoms, basically in Telco Industry, because of the criticity of services running on them.