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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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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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Detection of Anomalies in Pedestrian Walking
Pokorný, Ondřej ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
The goal of this work was to create a system that would be able to detect anomalies in pedestrian walking. As the core of my application, I have used OpenPose, which is an application for detecting human skeletons. Then I used a bidirectional LSTM neural network to detect anomalies in video sequences. This architecture was chosen during the experiment because it outperformed other solutions. I trained my model to detect three types of anomalies. The output of my application is a video with marked sequences of anomalies. The whole system is implemented in Python.
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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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Statistical anomaly detection methods of data communication
Woidig, Eduard ; Mangová, Marie (referee) ; Slavíček, Karel (advisor)
This thesis serves as a theoretical basis for a practical solution to the issue of the use of statistical methods for detecting anomalies in data traffic. The basic focus of anomaly detection data traffic is on the data attacks. Therefore, the main focus is the analysis of data attacks. Within the solving are data attacks sorted by protocols that attackers exploit for their own activities. Each section describes the protocol itself, its usage and behavior. For each protocol is gradually solved description of the attacks, including the methodology leading to the attack and penalties on an already compromised system or station. For the most serious attacks are outlined procedures for the detection and the potential defenses against them. These findings are summarized in the theoretical analysis, which should serve as a starting point for the practical part, which will be the analysis of real data traffic. The practical part is divided into several sections. The first of these describes the procedures for obtaining and preparing the samples to allow them to carry out further analysis. Further described herein are created scripts that are used for obtaining needed data from the recorded samples. These data are were analyzed in detail, using statistical methods such as time series and descriptive statistics. Subsequently acquired properties and monitored behavior is verified using artificial and real attacks, which is the original clean operation modified. Using a new analysis of the modified traffics compared with the original samples and an evaluation of whether it has been some kind of anomaly detected. The results and tracking are collectively summarized and evaluated in a separate chapter with a description of possible further attacks, which were not directly part of the test analysis.
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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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