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Behaviour-Based Identification of Network Devices
Polák, Michael Adam ; Holkovič, Martin (oponent) ; Polčák, Libor (vedoucí práce)
This thesis deals with the topic of identifying devices based on their behaviour. With the increasing number of devices on the network, it is becoming more and more important to be able to identify these devices based on their behaviour, due to the increased security risks. General networking concepts and multiple methods that have been used in the past to identify devices are discussed throughout the work. Subsequently, machine learning algorithms and their advantages and disadvantages are introduced. Finally, this thesis tests two traditional machine learning algorithms and proposes two new approaches to network device identification. The resulting final algorithm achieves the accuracy of 89% on a real life data-set with over 10,000 devices using a set of only eight features.
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Automatizované vyhledávání a uchovávání recenzí o produktech
Voráč, Tomáš
Diplomová práce se zabývá problémem automatizovaného vyhledání recenzí na webové stránce a dále také ukládáním nalezených recenzí. V práci jsou podrobně rozepsány možnosti ukládání nestrukturovaných dat a následný výběr nejvhodnějšího úložiště. Hlavní část práce se zabývá rozborem HTML struktury tak, aby bylo možné nalézt požadované informace na webové stránce. Tato práce se zabývá i způsoby určení podobnosti textových řetězců, aby bylo možné určit k jakému produktu nalezená recenze patří. K implementaci byl použit programovací jazyk Python.
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Behaviour-Based Identification of Network Devices
Polák, Michael Adam ; Holkovič, Martin (oponent) ; Polčák, Libor (vedoucí práce)
This thesis deals with the topic of identifying devices based on their behaviour. With the increasing number of devices on the network, it is becoming more and more important to be able to identify these devices based on their behaviour, due to the increased security risks. General networking concepts and multiple methods that have been used in the past to identify devices are discussed throughout the work. Subsequently, machine learning algorithms and their advantages and disadvantages are introduced. Finally, this thesis tests two traditional machine learning algorithms and proposes two new approaches to network device identification. The resulting final algorithm achieves the accuracy of 89% on a real life data-set with over 10,000 devices using a set of only eight features.
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