Počítačové systémy, ktoré musia poskytovať svoje služby s vysokou dostupnosťou vyžadujú isté bezpečnostné opatrenia na to, aby ostali dostupné aj pod paketovými sieťovými útokmi. Nevyžiadané pakety musia byť zahodené čo najskôr a čo najrýchlejšie. Táto práca analyzuje eXpress Data Path (XDP) ako techniku skorého zahodenia paketov a extended Berkeley Packet Filter (eBPF) ako mechanizmus rýchlej analýzy obsahu packetov. Poskytuje sa pohľad na dnešnú prax v oblasti firewallov v systémoch s linuxovým jadrom a navrhne sa systém rýchlej filtrácie paketov založený na eBPF a XDP. Do detailov popisujeme naimplementované filtračné riešenie. Nakoniec sa vyzdvihujú výhody XDP oproti ostatným súčasným technikám filtrácie paketov na sérii výkonnostných testov.

Extended Berkeley Packet Filter (eBPF) is an instruction set and an execution environment inside the Linux kernel. eBPF improves flexibility for data processing and is realized via a virtual machine featuring both a Just-In-Time (JIT) compiler and an interpreter running in the kernel. It executes custom eBPF programs supplied by the user, effectively moving kernel functionality into user space. eBPF has received widespread adoption by companies such as Facebook, Netflix, and academia for a wide range of application domains. eBPF can be used to program the eXpress DataPath (XDP), a kernel network layer that processes packets closer to the NetworkInterface Card (NIC) for fast packet processing. In this thesis, eBPF with XDP, and Iptables, are considered as a Network function(NF), implemented in a Virtual Machine (VM) for packet filtering. The traffic source(source VM) and traffic sink (destination VM) are present in the same subnet. The aim of this thesis is, to understand and investigate the implementation of NFs inVMs and to analyze performance metrics. In VirtualBox, VMs are created to implement the NFs. The results are obtained for the measurements that are essential for the performance evaluation of the NFs, and presented in graphs.

The world of computer networks is constantly evolving towards more efficient algorithms and more effective ways of using hardware resources. One of these evolutions is the eXpress Data Path (XDP). XDP is an interrupt based data path in the Linux kernel. XDP uses JIT-compiled programs in a virtual machine in a device driver context. Through XDP, fast packet processing is possible while still keeping the functionality of the Linux kernel intact. Therefore, this thesis aims to illuminate possible use cases for XDP in 5G simulators, as this real-world application of XDP is of interest. Specifically, use-cases where there is a need for fast packet processing. This thesis evaluates the use-cases using a performance evaluation of XDP and a literary review of 5G simulators, XDP, and technologies relating to XDP. This evaluation indicates that XDP is a candidate for packet processing in 5G simulators, specifically when compared to what performance is possible currently. This thesis argues from the performance evaluation and the literary review that XDP can be used for small programs, preferably data ingestion, in 5G simulators.

Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts. This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives. The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inexperienced developers for template-based ODP instantiation---neither of which are supported in prior tooling. The studies also resulted in improvements to ODP search engines based on ODP-specific attributes. Finally, the analysis shows that XD should include guidance for the developer roles and responsibilities in ontology engineering projects, suggestions on how to reuse existing ontology resources, and approaches for adapting XD to project-specific contexts.

Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts. This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives. The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inexperienced developers for template-based ODP instantiation---neither of which are supported in prior tooling. The studies also resulted in improvements to ODP search engines based on ODP-specific attributes. Finally, the analysis shows that XD should include guidance for the developer roles and responsibilities in ontology engineering projects, suggestions on how to reuse existing ontology resources, and approaches for adapting XD to project-specific contexts.

Ces dernières années, les thérapies ciblées pour le traitement du cancer, ont été introduites. Cependant, un médicament fonctionnant chez un patient peut ne pas fonctionner chez un autre. Pour éviter l'administration de traitements inefficaces, des méthodes capables de prédire les patients qui répondront à un médicament donné doivent être mises au point.Il n'est actuellement pas possible de prédire l'efficacité de la grande majorité des médicaments anticancéreux. L’apprentissage automatique (AA) est un outil particulièrement prometteur pour la médecine personnalisée. L’AA est un champ d’étude de l'intelligence artificielle ; elle concerne la mise au point et l'application d'algorithmes informatiques qui s'améliorent avec l'expérience. Dans ce cas, l'algorithme d’AA apprendra à faire la distinction entre les tumeurs sensibles et résistantes en fonction de plusieurs gènes au lieu d'un seul gène. Cette étude se concentre sur l'application de différentes approches de l’AA pour prédire la réponse à des médicaments anticancéreux des tumeurs et générer des modèles précis, biologiquement pertinentes et faciles à expliquer. / In recent years, targeted drugs for the treatment of cancer have been introduced. However, a drug that works in one patient may not work in another patient. To avoid the administration of ineffective treatments, methods that predict which patients will respond to a particular drug must be developed.Unfortunately, it is not currently possible to predict the effectiveness of most anticancer drugs. Machine learning (ML) is a particularly promising approach for personalized medicine. ML is a form of artificial intelligence; it concerns the development and application of computer algorithms that improve with experience. In this case, ML algorithm will learn to distinguish between sensitive and non-sensitive tumours based on multiple genes instead of a single gene. Our study focuses on applying different approaches of ML to predict drug response of tumours to anticancer drugs and generate models which have good accuracy, as well as are biologically relevant and easy to be explained.

Cílem této magisterské práce je návrh a implementace síťové sondy pro sledování toků na 10GbE rozhraní. Text se zabývá přehledem GNU/Linux nástrojů využívaných ve vysokorychlostních sítích a principů jejich fungování. Dále pak je uveden návrh a implementace sondy využívající mechanismu zero-copy pro sledování provozu na 10GbE rozhraní. Aplikace využívá Expresní datové cesty (XDP) a jeho AF_XDP soketu pro zachycení provozu na rozhraní. Jako testovací platforma byla vybrána platforma NETX používaná na FIT VUT.

V dnešnom svete zrýchľujúcej sa sieťovej prevádzky je potrebné držať krok v jej monitorovaní . Dostatočný prehľad o dianí v sieti dokáže zabrániť rozličným útokom na ciele nachádzajúce sa v nej . S tým nám pomáhajú systémy IDS, ktoré upozorňujú na udalosti nájdené v analyzovanej prevádzke . Pre túto prácu bol vybraný systém Suricata . Cieľom práce je vyladiť nastavenia systému Suricata s rozhraním AF_PACKET pre optimálnu výkonnosť a následne navrhnúť a implementovať optimalizáciu Suricaty . Výsledky z meraní AF_PACKET majú slúžiť ako základ pre porovnanie s navrhnutým vylepšením . Navrhovaná optimalizácia implementuje nové rozhranie založené na projekte Data Plane Development Kit ( DPDK ). DPDK je schopné akcelerovať príjem paketov a preto sa predpokladá , že zvýši výkon Suricaty . Zhodnotenie výsledkov a porovnanie rozhraní AF_PACKET a DPDK je možné nájsť na konci diplomovej práce .