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Statistical Analysis of Anomalies in Sensor Data
Gregorová, Kateřina ; Čmiel, Vratislav (referee) ; Sekora, Jiří (advisor)
This thesis deals with the failure mode detection of aircraft engines. The main approach to the detection is searching for anomalies in the sensor data. In order to get a comprehensive idea of the system and the particular sensors, the description of the whole system, namely the aircraft engine HTF7000 as well as the description of the sensors, are dealt with at the beginning of the thesis. A proposal of the anomaly detection algorithm based on three different detection methods is discussed in the second chapter. The above-mentioned methods are SVM (Support Vector Machine), K-means a ARIMA (Autoregressive Integrated Moving Average). The implementation of the algorithm including graphical user interface proposal are elaborated on in the next part of the thesis. Finally, statistical analysis of the results,the comparison of efficiency particular models and the discussion of outputs of the proposed algorithm can be found at the end of the thesis.
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Data Mining Case Study in Python
Stoika, Anastasiia ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis focuses on basic concepts and techniques of the process known as knowledge discovery from data. The goal is to demonstrate available resources in Python, which enable to perform the steps of this process. The thesis addresses several methods and techniques focused on detection of unusual observations, based on clustering and classification. It discusses data mining task for data with the limited amount of inspection resources. This inspection activity should be used to detect unusual transactions of sales of some company that may indicate fraud attempts by some of its salespeople.
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Deep Neural Networks for Defect Detection
Juřica, Tomáš ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The goal of this work is to bring automatic defect detection to the manufacturing process of plastic cards. A card is considered defective when it is contaminated with a dust particle or a hair. The main challenges I am facing to accomplish this task are a very few training data samples (214 images), small area of target defects in context of an entire card (average defect area is 0.0068 \% of the card) and also very complex background the detection task is performed on. In order to accomplish the task, I decided to use Mask R-CNN detection algorithm combined with augmentation techniques such as synthetic dataset generation. I trained the model on the synthetic dataset consisting of 20 000 images. This way I was able to create a model performing 0.83 AP at 0.1 IoU on the original data test set.
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Unified Reporting for Performance Testing
Kůrová, Martina ; Vojnar, Tomáš (referee) ; Šimková, Hana (advisor)
Moderní pokrok v oblasti technologií pro vývoj dnešních softwarových aplikací umožnil vývojářům více se soustředit na vývoj funkčnosti aplikace na úkor sledování jejího výkonu a správy zdrojů. V důsledku toho se zvýšily požadavky na nástroje pro výkonnostní testování, které by měly poskytovat vývojářům jasný a srozumitelný přehled o stavu systému z hlediska jeho výkonu a umožnit rychlou interpretaci naměřených výsledků. Tato práce zkoumá typické výkonnostní problémy dnešních aplikací a navrhuje přístupy, pomocí kterých je možné tyto anomálie automaticky rozpoznat. Pomocí statistických metod, jako je regresní a korelační analýza, je provedena analýza dat naměřených během výkonnostního testování s cílem rozpoznat ve výsledcích odchylky od normálního chování a z nich identifikovat výkonnostní problémy. Výsledkem je report o celkovém stavu systému z hlediska jeho výkonu. Implementací regresní analýzy je možné detekovat výkonnostní problémy jako je například zhoršující se reakční čas odpovědi, nízká propustnost systému či odhalit únik paměti. Navrhovaný přístup byl implementován v podobě nové komponenty v open-source nástroji pro výkonnostní testování PerfCake. Vyvinutá komponenta je schopna detekovat a reportovat potenciální výkonnostní problémy a jejich pravděpodobnost.
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System for Detection of APT Attacks
Hujňák, Ondřej ; Kačic, Matej (referee) ; Barabas, Maroš (advisor)
The thesis investigates APT attacks, which are professional targeted attacks that are characterised by long-term duration and use of advanced techniques. The thesis summarises current knowledge about APT attacks and suggests seven symptoms that can be used to check, whether an organization is under an APT attack. Thesis suggests a system for detection of APT attacks based on interaction of those symptoms. This system is elaborated further for detection of attacks in computer networks, where it uses user behaviour modelling for anomaly detection. The detector uses k-nearest neighbors (k-NN) method. The APT attack recognition ability in network environment is verified by implementing and testing this detector.
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Portscan Detection in High-Speed Networks
Kapičák, Daniel ; Kekely, Lukáš (referee) ; Bartoš, Václav (advisor)
In this thesis, I present the method to efficiently detect TCP port scans in very high-speed links. The main idea of this method is to discard most of the handshake packets without loss in accuracy. With two Bloom filters that track active destinations and TCP handshakes, the algorithm can easily discard about 80\% of all handshake packets with negligible loss in accuracy. This significantly reduces both the memory requirements and CPU cost. Next, I present my own extension of this algorithm, which significantly reduces the number of false positives caused by the lack of communication from the server to the client. Finally, I evaluated this algorithm using packet traces and live traffic from CESNET . The result showed that this method requires less than 2 MB to accurately monitor very high-speed links, which perfectly fits in the cache memory of today's processors.
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Comparison of Network Anomaly Detection Methods
Pacholík, Václav ; Grégr, Matěj (referee) ; Bartoš, Václav (advisor)
This thesis focuses on methods for detection of network traffic anomalies. The preamble contains a short overview of all categories along with their corresponding examples. The next part details the three methods chosen for comparison: EWMA, Holt-Winters and the wavelet-based method. Furthermore are described generated input data attacks that were, along with the already discovered ones, used for rating of the compared methods detection abilities. Finally, optimal parameters are described along with other discovered flaws including suggestions for improvement.
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Network Traffic Analysis Based on Clustering
Černý, Tomáš ; Drahošová, Michaela (referee) ; Bartoš, Václav (advisor)
This thesis focuses on anomaly detection in network traffic using clustering methods. First, basic anomaly detection methods are introduced. The next part describes hierarchical and k-means clustering in detail. Also there are described selected normalization techniques. Part is given to the procedure for detecting anomalies in the context of data mining. Furthermore a few words about implementation of single methods. Finally, clustering methods and normalization techniques are tested and compared.
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