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Browser and User Fingerprinting for Practical Deployment
Vondráček, Tomáš ; Malinka, Kamil (referee) ; Polčák, Libor (advisor)
The aim of the diploma thesis is to map the information provided by web browsers, which can be used in practice to identify users on websites. The work focuses on obtaining and subsequent analysis of information about devices, browsers and side effects caused by web extensions that mask the identity of users. The acquisition of information is realized by a designed and implemented library in the TypeScript language, which was deployed on 4 commercial websites. The analysis of the obtained information is carried out after a month of operation of the library and focuses on the degree of information obtained, the speed of obtaining information and the stability of information. The dataset shows that up to 94 % of potentially different users have a unique combination of information. The main contribution of this work lies in the created library, design of new methods of obtaining information, optimization of existing methods and the determination of quality and poor quality information based on their level of information, speed of acquisition and stability over time.
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Detection of anomalies in network traffic using compression methods
Blažek, Libor ; Dvořák, Jan (referee) ; Blažek, Petr (advisor)
The objective of the thesis is to design a practical demonstration of the functionality of selected compression methods. The following chapters will discuss the attacks on terminals and mentioned some measures. The show will be processed using two methods development environment. The attacks will detect anomalies in the network and subsequently carried out at one of the sample data compression methods. Data will be collected as normal operation at the terminal station, and then in the attack.
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Anomaly detection by neural networks
Strakoš, Jan ; Sikora, Marek (referee) ; Blažek, Petr (advisor)
This bachelor thesis is focused on anomaly detection represented as computer network attacks by neural network. One of the most common groups of attacks is Distributed Denial of Service (DDoS) attacks, which the system based on neural network should identificate. In the theoretical part of this thesis are described legitimate, non-standard and illegitimate traffic. Another part of this chapter described DDoS attacks, options of their detection, neural networks principle and their use. Practical part describe choosed communication parameters, specifying the threshold intervals of legitimate traffic, constructing a neural network which use of these parameters and threshold intervals, implementation of neural network into the system and presenting results.
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Effective Network Anomaly Detection Using DNS Data
Fomiczew, Jiří ; Žádník, Martin (referee) ; Kováčik, Michal (advisor)
This thesis describes the design and implementation of system for effective detection of network anomaly using DNS data. Effective detection is accomplished by combination and cooperation of detectors and detection techniques. Flow data in NetFlow and IPFIX formats are used as input for detection. Also packets in pcap format can be used. Main focus is put on detection of DNS tunneling. Thesis also describes Domain Name System (DNS) and anomalies associated with DNS.
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Knowledge Discovery in Spatio-Temporal Data
Pešek, Martin ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with knowledge discovery in spatio-temporal data, which is currently a rapidly evolving area of research in information technology. First, it describes the general principles of knowledge discovery, then, after a brief introduction to mining in the temporal and spatial data, it focuses on the overview and description of existing methods for mining in spatio-temporal data. It focuses, in particular, on moving objects data in the form of trajectories with an emphasis on the methods for trajectory outlier detection. The next part of the thesis deals with the process of implementation of the trajectory outlier detection algorithm called TOP-EYE. In order to testing, validation and possibility of using this algorithm is designed and implemented an application for trajectory outlier detection. The algorithm is experimentally evaluated on two different data sets.
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Network Traffic Analysis Based on Sketches
Dřevo, Aleš ; Kekely, Lukáš (referee) ; Bartoš, Václav (advisor)
Aim of this thesis is to create a program for network traffic analysis and for detection of anomallies in the traffic. The Heavy-Changes Detection technique which falls within the Data stream algorithm category is used to do so. Special structures called sketches are used for data processing. These structures are capable of maintaining large amounts of data with low memory consumption. Programs from Nemea system for which this project is created are used for gathering necessary network data.
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Specific anomaly detection methods in wireless communication networks
Holasová, Eva ; Blažek, Petr (referee) ; Fujdiak, Radek (advisor)
The diploma thesis is focuses on technologies and security of the wireless networks in standard IEEE 802.11, describes the most used standards, definition of physical layer, MAC layer and specific technologies for wireless networks. The diploma thesis is focused on description of selected security protocols, their technologies as well as weaknesses. Also, in the thesis, there are described security threats and vectors of attacks towards wireless networks 802.11. Selected threats were simulated in established experimental network, for these threats were designed detection methods. For testing and implementing designed detection methods, IDS system Zeek is used together with network scripts written in programming language Python. In the end there were trained and tested models of machine learning both supervised and unsupervised machine learning.
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Parametrization of network attacks
Jelínek, Michael ; Sikora, Pavel (referee) ; Blažek, Petr (advisor)
This bachelor thesis is dedicated to the definition of suitable parameters for network attack identification with the use of neural networks. In the theoretical part of this thesis are methods for anomaly detection in network communication, structure of artificial neural networks and DDoS attacks used for verification of detection capabilities. The practical part of this thesis is focused on the process of preparing data, the subsequent implementation into neural networks and a summary of the results achieved for the different setups of neural networks.
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Detection of DoS and DDoS attacks targeting a web server
Nguyen, Minh Hien ; Fujdiak, Radek (referee) ; Kuchař, Karel (advisor)
The bachelor thesis deals with the detection of DoS (Denial of service) and DDoS (Distributed Denial of Service) attacks targeting a web server. This work aims to design detection methods, which will be subsequently tested. Analysis of attacks according to the ISO/OSI (International Organization for Standardization/Open Systems Interconnection) reference model will allow an understanding of the features of individual attacks. In the practical part, some tools are used to implement attacks, then there are generators of legitimate network traffic and a secure web server. Substantial data are created from ongoing attacks and communications of ordinary users. These data are an important part of the proposed methods. The purpose of assessing the achieved results is to evaluate the effectiveness of individual detection methods in terms of accuracy and time consumption.
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