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Monitoring of the network performance in highspeed Internet infrastructure is a challenging task, as the requirements for the given quality level are service-dependent. Backbone QoS monitoring and analysis in Multi-hop Networks requires therefore knowledge about types of applications forming current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown to have ability to distinguish between 7 different applications in test set of 76,632-1,622,710 unknown cases with average accuracy of 99.3-99.9 %. This high accuracy was achieved by using high quality training data collected by our system, a unique set of parameters used for both training and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options. This paper shows how we collected accurate traffic data, presents arguments used in classification process, introduces the C5.0 classifier and its options, and finally evaluates and compares the obtained results.

During the last decade significant scientific efforts have been invested in the development of methods that could provide efficient and effective botnet detection. As a result, an array of detection methods based on diverse technical principles and targeting various aspects of botnet phenomena have been defined. As botnets rely on the Internet for both communicating with the attacker as well as for implementing different attack campaigns, network traffic analysis is one of the main means of identifying their existence. In addition to relying on traffic analysis for botnet detection, many contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine learning-based botnet detection systems.

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Increasingly public bodies and organizations are publishing Open Data for citizens to improve their quality of life and solving public problems. But having Open Data available is not enough. Public engagement is also important for successful Open Data initiatives. There is an increasing demand for strategies to actively involve the public exploiting Open Data, where not only the citizens but also school pupils and young people are able to explore, understand and extract useful information from the data, grasp the meaning of the information, and to visually represent findings. In this research paper, we investigate how we can equip our younger generation with the essential future skills using Open Data as part of their learning activities in public schools. We present the results of a survey among Danish school teachers and pupils. The survey focuses on how we can introduce Open Data visualizations in schools, and what are the possible benefits and challenges for pupils and teachers to use Open Data in their everyday teaching environment. We briefly review Copenhagen city's Open Data and existing open source software suitable for visualization, to study which open source software pupils can easily adapt to visualize Open Data and which data-sets teachers can relate to their teaching themes. Our study shows that introducing Open Data visualizations in schools make everyday teaching interesting and help improving pupils learning skills and that to actively use Open Data visualizations in schools, teachers and pupils need to boost their digital skills.

Private and public institutions are using open and public data to provide better services, which increases the impact of open data on daily life. With the advancement of technology, it becomes also important to equip our younger generation with the essential skills for future challenges. In order to bring up a generation equipped with 21st century skills, open data could facilitate educational processes at school level as an educational resource. Open data could acts as a key resource to enhance the understanding of data through critical thinking and ethical vision among the youth and school pupils. To bring open data into schools, it is important to know the teacher's perspective on open data literacy and its possible impact on pupils. As a research contribution, we answered these questions through a Danish public school teacher's survey where we interviewed 10 Danish public school teachers of grade 5-7th and analyzed their views about the impact of open data on pupils' learning development. After analyzing Copenhagen city's open data, we identified four open data educational themes that could facilitate different subjects, e.g. geography, mathematics, basic science and social science. The survey includes interviews, open discussions, questionnaires and an experiment with the grade 7th pupils, where

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