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The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

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Identifying a network misuse takes days or even weeks, and network administrators usually neglect zero-day threats until a large number of malicious users exploit them. Besides, security applications, such as anomaly detection and attack mitigation systems, must apply real-time monitoring to reduce the impacts of security incidents. Thus, information processing time should be as small as possible to enable an effective defense against attacks. In this paper, we present a fast preprocessing method for network traffic classification based on feature correlation and feature normalization. Our proposed method couples a normalization and a feature selection algorithms. We evaluate the proposed algorithms against three different datasets for eight different machine learning classification algorithms. Our proposed normalization algorithm reduces the classification error rate when compared with traditional methods. Our Feature Selection algorithm chooses an optimized subset of features improving accuracy by more than 11% within a 100-fold reduction in processing time when compared to traditional feature selection and feature reduction algorithms. The preprocessing method is performed in batch and streaming data, being able to detect concept-drift.

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Abstract-Managing computer networks is challenging because of the numerous monitoring variables and the difficulty to autonomously configure network parameters. This paper presents the OpenFlow MaNagement Infrastructure (OMNI), which helps the administrator to control and manage OpenFlow networks by providing remote management based on a web interface. OMNI provides flow monitoring and dynamic flow configuration through a service-oriented architecture. OMNI also offers an Application Programming Interface (API) for collecting data and configuring the OpenFlow network. We propose a multi-agent system based on OMNI API that reduces packet loss rates. We evaluate both the OMNI management applications and the multi-agent system performance using a testbed. Our results show that the multiagent system detects and reacts to a packet-loss condition in less than three monitoring intervals.

Abstract-Internet Service Providers resist innovating in the network core, fearing that deploying a new protocol or service compromises the network operation and their profit, as a consequence. Therefore, a new Internet model, called Future Internet, which enables core innovation, must accommodate new protocols and services with the current scenario, isolating each protocol stack from others. Virtualization is the key technique that provides concurrent protocol stack capability to the Future Internet elements. In this paper, we evaluate the performance of three widespread virtualization tools, Xen, VMware, and OpenVZ, considering their use for router virtualization. We conduct experiments with benchmarking tools to measure the overhead introduced by virtualization in terms of memory, processor, network, and disk performance of virtual routers running on commodity hardware. We also evaluate the effects of the increasing number of virtual machines on Xen network virtualization mechanism. Our results show that Xen best fits virtual router requirements. Moreover, Xen fairly shares the network access among virtual routers, but needs further enhancement when multiple virtual machines simultaneously forward traffic.