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Wireless Local Area Network (WLAN) infrastructure is a dominant technology for direct access to the Internet and for cellular mobile data traffic offloading to WLANs. Additionally, the enterprise infrastructure can be used to provide functionality for the Internet of Things and Machine to Machine scenarios. This work is focused on improvements of radio resources control scalability similar to mobile networks via handover between cells. We introduce an improved IEEE 802.11 architecture utilizing Software-Defined Networks (SDNs). The proposed architecture allows communications during device movements without losing a quality of service (QoS). The fast seamless handover with QoS enables efficient usage of radio resources in large networks. Our improvements consist of integrating wireless management to OpenFlow protocol, separating encryption and decryption from an access point. In parallel, this feature as a side effect unloads processing at the Access Points (APs). Finally, the functionality of architecture design and scalability was proven by Colored Petri Nets (CPNs). The second proof of our concept was performed on two scenarios. The first scenario was applied to a delay sensitive use case. The second scenario considers a network congestion in real world conditions. Client’s mobility was integrated into both scenarios. The design was developed to demonstrate SDN WLAN architecture efficiency.

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Students education in various fields (such as science, technology, engineering, and mathematics) is constantly looking for ways and techniques on how to motivate students to learn, how to increase their engagement and how to increase the e ciency of knowledge acquisition. Information and communication technologies are developing at a very rapid speed and o er many new opportunities that could be used for such purposes. This paper focuses on virtual laboratory technologies that could be very helpful for these learning problems especially for subjects that lose the interest of young people. We have conducted two pilots at a Slovak secondary school and university to analyse the usability of self-directed learning applied to teach networking topics such as software-defined networking and network functions virtualisation. This learning approach was enhanced by a developed virtual lab and a set of self-tests. Knowledge tests and questionnaires have been used to investigate the impact of this self-directed virtual lab-based learning approach on students motivation to learn, feelings, satisfaction with learning approach and knowledge gain. The results showed that students appreciated the virtual laboratory for improving learning and their motivation to learn and their knowledge acquisition was noticeably improved when the virtual lab was included.

Part 1: Networks and System ArchitectureInternational audiencePresented work focuses onto proposal, implementation and evaluation of the new method for detection and type identification of SYN flood (DoS) attacks. The method allows distinguishing type of detected SYN flood attacks – random, subnet or fixed. Based on Counting Bloom filter, the attack detection and identification algorithm is proposed, implemented and evaluated in KaTaLyzer network traffic monitoring tool. Proof of correctness of the approach for TCP SYN flood attack detection and type identification is provided – both in practical and theoretical manners. In practice, new module for KaTaLyzer is implemented and TCP attacks are detected, identified and network administrator is notified about them in real-time