National Repository of Grey Literature 246 records found  previous11 - 20nextend  jump to record: Search took 0.00 seconds. 
Internet Domain Risk Evaluation
Polišenský, Jan ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
Internet domains play a crucial role in providing web services. It is necessary to be able to detect malicious domains. The aim of this thesis is to summarize current work on domain classification and develop improved system of classification. This system is implemented using support vector machines a neural networks and shows 96.3% accuracy on test data.
Characterization of Network Operation of Computers and Their Groups
Kučera, Rostislav ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
The aim of this work is to implement a module for detecting DDoS attacks. The module pro- cesses network traffic, processes it, stores its profile, from which statistical data used for the detection itself are subsequently calculated. The work also deals with the implementation of the module for intrusion detection system Suricata.
Behavioral Analysis of DDoS Network Attacks
Kvasnica, Ondrej ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
This bachelor thesis deals with anomaly detection in computer networks using artificial intelligence method. Main focus is on the detection of DDoS attacks based on the information from the lower layers of the OSI model. The target is to design and implement a system that is capable of detecting different types of DDoS attacks and characterize common features among them. Selected attacks are SYN flood, UDP flood and ICMP flood. Description and feature selection of the attacks is included. Furthermore, a system is designed that evaluates whether the network traffic (organized into flows) is a DDoS attack or not. Attacks are detected using the XGBoost method, which uses supervised learning. The final model is validated using cross-validation and tested on attacks generated by the author.
System for the Management of Obligations Under the GDPR Regulation
Bojnanský, Matej ; Rychlý, Marek (referee) ; Očenášek, Pavel (advisor)
The aim of this work is to create a web application to manage the obligations of companies to GDPR regulations. The system consists of two parts. The first part is a company that uses software services and the second part is a company that provides legal action with regard to GDPR. The following technologies were used to create the application: PHP, Laravel, HTML, Bootstrap, CSS, HTML, MySQL, JavaScript, Apache.
Application-based Anomalous Communication Detection
Dostál, Michal ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
This bachelor thesis deals with the analysis, design and implementation of a system for detecting anomalous network communication activities using high-level characteristics. The thesis contains a theoretical basis for the detection of anomalies using countries, autonomous systems and applications that are used to communicate. It also contains information about the techniques and methods of machine learning needed for implementation. The practical part describes the design, use and implementation of individual technologies. The result of this work is detection based on multiple machine learning methods, mostly classification.
Scalable 1-out-of-k Blockchain-Based Voting with Privacy of Votes
Stančíková, Ivana ; Očenášek, Pavel (referee) ; Homoliak, Ivan (advisor)
The main subject of this work is the assessment of electronic voting systems with regard to their required and achieved properties. The goal of this project is designing an electronic voting protocol that satisfies the requirements for privacy protection while also being scalable and fault-tolerant. Existing protocols are examined and compared according to their properties. The design proposed in this work uses smart contracts on blockchain and combines the approaches from the examined solutions. Scalability is achieved by dividing the process of voting between several smart contracts. Each of these contracts carries out the voting in small scale with only a subset of voters and the partial results are then aggregated. The problem of finding a suitable platform for implementation of the proposed protocol is also addressed in this work.
Log Analysis and Hardware Utilization
Kuchyňka, Jiří ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
The goal of this thesis is to design and implement a system for long-term monitoring of the state of Linux systems located in a production environment. The thesis focuses mainly on situation in which the system does not have the ability to send the collected data for analysis over the network, so data collection must be completely automatic and data must be transferred from monitored systems to a central system for collection, analysis and visualization. A substantial part of the work is devoted to the design and implementation of a web application used to export data from monitored systems to the transmission medium and import them from it to the system for data collection. The resulting solution aims to simplify the collection of data from systems, previously performed directly by system administrators, so that it can be performed by anyone who can physically approach the monitored system and thus reduce the costs associated with monitoring these remote systems.
Analysis of Malicious Encrypted Network Traffic
Dubec, Branislav ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
This bachelor thesis deals with the analysis of malicious encrypted network traffic using artificial intelligence methods. A solution is to create a system for detecting security intrusions using detection analysis methods. Theoretical part describes methods of anomaly detection, and explains the concept of artificial neural network. In the practical part, it experiments with various anomaly detection techniques in order to obtain the best results.
Anomaly Detection Based on SNMP Communication
Štěpán, Daniel ; Drga, Jozef (referee) ; Očenášek, Pavel (advisor)
The aim of this thesis was to develop a practically applicable set of methods for classification and detection of anomalies in computer network environments. I have created extensions to the network monitoring system in the form of two modules for an open source network monitoring tool based on machine learning. The created modules can learn the characteristics of normal network traffic. The first module, based on the algorithm Random Forest Classifier, detects and is able to classify several known denial-of-service attacks. The second module, based on the algorithm Local Outlier Factor, detects anomalous levels of network traffic. Attacks that the first module is able to classify are the following: TCP SYN flood, UDP flood and ICMP flood. Moreover, it was trained to detect the SSH Bruteforce attacks and the slow and fragmented Slowloris attack. While working on this thesis, I tested the device using the methods mentioned above. The experiments showed that the classification-based module is able to detect known attacks, except for the Slowloris attack, whose characteristics are not very different from normal traffic. The second module sucessfully detects higher levels of network traffic, but does not perform the classification.
Automated Testing of Smart Cards
Yadlouski, Pavel ; Očenášek, Pavel (referee) ; Homoliak, Ivan (advisor)
Tato bakalářská práce se zabývá automatizovaným testováním podpory Smart Karet v RHEL. Problém manuálního testování je vyřešen vytvořením nové testovací knihovny. Tato knihovna je zodpovědná za konfiguraci testovacího prostředí a poskytuje testerovi rozhraní pro automatizovanou manipulaci s tímto prostředím. Jako výsledek jsme vytvořili univerzální knihovnu pro testování podpory smart karet. Primárním cílem je implementace samotné knihovny, pak následující převod existujících manuálních testů do kódu za pomoci teto knihovny a zprovozněni těchto testů ve vnitřní pipelině Red Hat.

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