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HTTP Application Performance Monitoring
Knapik, Martin ; Kováčik, Michal (referee) ; Bartoš, Václav (advisor)
Goal of this bachelor thesis was to create solution for monitoring and analysis of network performance of HTTP server using Nemea framework and NetFlow data. For this purpose, I've created Nemea module for filtering, parsing and saving NetFlow data enhanced by informations gained from HTTP plugin on exporter. For analysis and user interface, webpage based on Django framework was created, used for displaying statistics that are useful for users in order to reveal problems with monitored servers. Result of my work is product, which is demonstrating possibility of using of Nemea system for passive monitoring of HTTP servers.
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Detection of Peer-to-Peer Communication
Letý, Pavel ; Kováčik, Michal (referee) ; Bartoš, Václav (advisor)
This thesis is focused on issues in detection of P2P network from NetFlow. In the theoretical part of this work are introduced actual techniques in detection of this communication in network. There are presented their advantages and disadvantages too. The biggest attention is focused on the classification scheme of Mr. Bashir which deals with a detection of a protocol BitTorrent and a Skype application from Netflow. Following this scheme is designed a detection module for a modular system of a traffic analysis Nemea, developed by Cesnet organization. In the practical part of this work is introduced the implementation of this module. There are also presented results of experiments with real data.
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Detection of Cryptocurrency Miners Based on IP Flow Analysis
Šabík, Erik ; Krobot, Pavel (referee) ; Žádník, Martin (advisor)
This master’s thesis describes the general information about cryptocurrencies, what principles are used in the process of creation of new coins and why mining cryptocurrencies can be malicious. Further, it discusses what is an IP flow, and how to monitor networks by monitoring network traffic using IP flows. It describes the Nemea framework that is used to build comprehensive system for detecting malicious traffic. It explains how the network data with communications of the cryptocurrencies mining process were obtained and then provides an analysis of this data. Based on this analysis a proposal is created for methods capable of detecting mining cryptocurrencies by using IP flows records. Finally, proposed detection method was evaluated on various networks and the results are further described.
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Module for Network Policy Monitoring in Flow Data
Piecek, Adam ; Kučera, Jan (referee) ; Wrona, Jan (advisor)
The aim of this master's thesis is to design a language through which it would be possible to monitor a stream of network flows in order to detect network policy violations in the local network. An analysis of the languages used in the data stream management systems and an analysis of tasks submitted by the potential administrator were both carried out. The analysis specified resulted in the language design which represents pipelining consisting of filtering and aggregation. These operations can be clearly defined and managed within security rules. The result of this thesis also results in the Policer modul being integrated in the NEMEA system, which is able to apply the main commands of the proposed language. Finally, the module meets the requirements of the specified tasks and may be used for further development in the area of monitoring network policies.
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Detection of Malicious Domain Names
Setinský, Jiří ; Perešíni, Martin (referee) ; Tisovčík, Peter (advisor)
The bachelor thesis deals with the detection of artificially generated domain names (DGA). The generated addresses serve as a means of communication between the attacker and the infected computer. By detection, we can detect and track infected computers on the network. The detection itself is preceded by the study of machine learning techniques, which will then be applied in the creation of the detector. To create the final classifier in the form of a decision tree, it was necessary to analyze the principle of DGA addresses. Based on their characteristics, the attributes were extracted, according to which the final classifier will be decided. After learning the classification model on the training set, the classifier was implemented in the target platform NEMEA as a detection module. After final optimizations and testing, we achieved a accuracy of the classifier of 99%, which is a very positive result. The NEMEA module is ready for real-world deployment to detect security incidents. In addition to the NEMEA module, another model was created to predict the accuracy of datasets with domain names. The model is trained based on the characteristics of the dataset and the accuracy of the DGA detector, whose behavior we want to predict.
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Profiling of Network Traffic for DDoS Mitigation
Ligocká, Alexandra ; Tisovčík, Peter (referee) ; Žádník, Martin (advisor)
The aim of this work is to propose metrics for \gls{ddos} attacks detection and setting the thresholds of normal network traffic in a given computer network at different levels of detail. Based on the selected metrics and network flow data, a network profile is extracted and afterwards stored in memory. Within the implementation part, this work deals with the implementation of program for the collection and calculation of specified metrics, their processing, storage and provides a simple interface providing access to stored data.
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Anomaly Detection in IoT Networks
Halaj, Jozef ; Hujňák, Ondřej (referee) ; Kořenek, Jan (advisor)
The goal of the thesis was an analysis of IoT communication protocols, their vulnerabilities and the creation of a suitable anomaly detector. It must be possible to run the detector on routers with the OpenWRT system. To create the final solution, it was necessary to analyze the communication protocols BLE and Z-Wave with a focus on their security and vulnerabilities. Furthermore, it was necessary to analyze the possibilities of anomaly detection, design and implement the detection system. The result is a modular detection system based on the NEMEA framework. The detection system is able to detect re-pairing of BLE devices representing a potential pairing attack. The system allows interception of Z-Wave communication using SDR, detection of Z-Wave network scanning and several attacks on network routing. The system extends the existing detector over IoT statistical data with more detailed statistics with a broader view of the network. The original solution had only Z-Wave statistics with a limited view of the network obtained from the Z-Wave controller. The modular solution of the system provides deployment flexibility and easy system scalability. The functionality of the solution was verified by experiments and a set of automated tests. The system was also successfully tested on a router with OpenWRT and in the real world enviroment. The results of the thesis were used within the SIoT project.
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Detection of Slow HTTP DoS Attacks
Jakubíček, Patrik ; Kováčik, Michal (referee) ; Bartoš, Václav (advisor)
This thesis deals with the detection of Slowloris attack. Based on the findings a detection module for Nemea system is implemented. It analyzes flow records and performs attack detection. Tests have verified that the module can work in real deployment and detect Slowloris attack quite successfully.
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Fingerprinting and Identification of TLS Connections
Hejcman, Lukáš ; Kocnová, Jitka (referee) ; Kekely, Lukáš (advisor)
TLS je dnes nejpopulárnější šifrovací protokol používaný na internetu. Jeho cílem je poskytnout vysokou úroveň zabezpečení a soukromí pro komunikaci mezi zařízeními. Představuje však výzvu z hlediska monitorování a správy sítí, protože není možné analyzovat komunikaci šifrovanou pomocí tohoto protokolu ve velkém měřítku, pomocí existujících metod založených na detailní analýze obsahu paketů. Analýza šifrované komunikace může správcům pomoci detekovat škodlivou aktivitu v jejich sítích a také jim může pomoci identifikovat potenciální bezpečnostní hrozby. V této práci představuji metodu, která nám umožňuje využít výhod dvou metod otisků TLS, JA3 a Cisco Mercury, k určení operačního systému a procesů klientů v počítačové síti. Navržená metoda je schopna dosáhnout srovnatelných nebo lepších výsledků v porovnání se stávajícím přístupem Cisco Mercury pro vybrané datové sady a zároveň poskytuje možnosti pro detailnější analýzy klasifikací než JA3. V rámci práce je dále implementován modul pro systém NEMEA, který je schopný analyzovat TLS provoz pomocí nově navrženého přístupu.
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IP Address Activity Monitoring
Pilátová, Kateřina ; Krobot, Pavel (referee) ; Bartoš, Václav (advisor)
Poslední dobou se objem přenášených dat po síti neustále zvyšuje. K urychlení prohledávání dat je potřeba mít způsob jejich vhodné indexace. Tato bakalářská práce se zabývá tímto problémem, konkrétně ukládáním a vyhledáváním dat za účelem zjištění aktivity komunikujících IP adres. Cílem této práce je navrhnout a implementovat systém pro efektivní dlouhodobé ukládání a vizualizaci aktivity IP adres. Aktivitou je myšleno, zda daná adresa generovala provoz v daném intervalu či ne, tedy lze ji reprezentovat jediným bitem, což redukuje objem prohledávaných dat. Výsledný systém se skládá z backendu monitorujícího provoz a ukládajícího záznamy o aktivitě do uložiště a jejich parametry do konfiguračního souboru. Dále obsahuje webový server, který na základě požadavků uživatele data čte a vizualizuje ve formě obrázků. Uživatel může specifikovat oblast dat, kterou chce zkoumat podrobněji, pomocí interaktivního webového rozhraní.
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