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HTTP Application Anomaly Detection
Rádsetoulal, Vlastimil ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
The goal of this work is to introduce anomaly detection principles and review its possibilities, as one of the intrusion detection methods in HTTP traffic. This work contains theoretical background crucial for performing an anomaly detection on HTTP traffic, and for utilising neural networks in achieving this goal. The work proposes tailored design of an anomaly detection model for concrete web server implementation, describes its implementation and evaluates the results. The result of this work is successful initial experiment, of modeling normal behavior of HTTP traffic and creation of the mechanism, capable of detection of anomalies within future traffic.
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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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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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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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Anomaly Detection in Generated Incident Ticket Volumes
Šurina, Timotej ; Rychlý, Marek (referee) ; Trchalík, Roman (advisor)
Táto bakalárska práca sa zaoberá problematikou detekcie anomálií v časových radoch. Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.
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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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The weather and stock returns
Černý, Patrik ; Kukačka, Jiří (advisor) ; Čornanič, Aleš (referee)
This thesis examines a behavioral finance topic, the effect of weather on stock returns. The research was performed with the aim to verify formerly published results of various weather variables like sunshine, precipitation or temperature influencing stock markets. For the analysis Ordinary Least Squares regressions were implemented to investigate the relationships of stock returns and weather variables proposed in the previous literature as well as other market efficiency effects, a Monday and a January effect. In addition, GARCH model was carried out to check the influence of weather conditions on stock return volatility. Data used for the analysis consists of 24 emerging and 23 developed markets worldwide in the period 2006-2017. The results are not in support of the theory of weather affecting market trading which corresponds to the market efficiency theory. There seems to be no difference between the developed and emerging countries, not even countries' land area plays a role. However, in the thesis repeatedly appears significant evidence of the presence of the Monday effect. Keywords Behavioral finance, Weather effect, Market efficiency, Anomaly, GARCH 1
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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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Detection of Unusual Events in Temporal Data
Černík, Tomáš ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
Bachelor thesis deals with detection of unusual events (anomalies) in available temporal data. Theoretical part describes existing techniques and algorithms used to detect outliers. There are also introduced meteorological data that are after that used for experimental verification of implemented detection algorithms. Second part, practical one, describes design and implementation of application and algorithms. Algorithms are also tested in search for point, contextual and collective anomalies.
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