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Implement Security Service for Preventing Internet Attacks
Fajkus, Karel ; Trchalík, Roman (referee) ; Očenášek, Pavel (advisor)
The main purpose of this work is to design and implement a system, that would allow to ban users based on their actions. Currently, cyber attacks have become very common, which leads to a necessity to develop great application and system protections. This project offers a solution, that could decrease a number of cyber attacks, and also prevent banning common users because of the same public IP address as attacker.
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Application Service and Server Monitoring
Pleško, Filip ; Burget, Radek (referee) ; Rychlý, Marek (advisor)
This bachelor thesis addresses an issue of monitoring servers and server aplications. The goal is to simplify this monitoring and make it more efficient. To do so, we used ELK stack. With the use of this stack we achieved to easily read from log files and show usefull informations from them.
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Performance Monitoring of MES PHARIS
Ondráček, Aleš ; Hruška, Martin (referee) ; Smrčka, Aleš (advisor)
This diploma thesis deals with the performance monitoring of automated development processes and performance testing of the MES PHARIS system. The main scope of thesis is the collection of data on tasks performed on automation servers DevOps and Jenkins, processing of this data and their subsequent visualization. The second part of the diploma thesis deals with the processing of data from performance testing and their appropriate representation using visualization. The core technology that is used is ELK Stack.
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Performance Data Collection of MES PHARIS
Oháňka, Martin ; Hruška, Martin (referee) ; Smrčka, Aleš (advisor)
This master's thesis deals with monitoring of automated tasks on integration servers and obtaining data from these tasks. Another area of this work is performance testing and to obtain information about hardware utilization from it. Thanks to this, it is possible to perform performance analysis of the implemented solution from different performance perspectives. The result of this master's thesis is a software solution that can obtain data about tasks from DevOps and Jenkins integration servers. In the area of performance testing, there is created a solution for parallel execution of tasks. The output of this work an output passed in JSON format. The data is then transferred to the Elastic platform, specifically Logstash, where it is subsequently visualized using Kibana. The Beat platform is used to collect data from performance testing. The solution was applied to the production information system MES PHARIS of the UNIS company.
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Log Analysis and Hardware Utilization
Kuchyňka, Jiří ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
The goal of this thesis is to design and implement a system for long-term monitoring of the state of Linux systems located in a production environment. The thesis focuses mainly on situation in which the system does not have the ability to send the collected data for analysis over the network, so data collection must be completely automatic and data must be transferred from monitored systems to a central system for collection, analysis and visualization. A substantial part of the work is devoted to the design and implementation of a web application used to export data from monitored systems to the transmission medium and import them from it to the system for data collection. The resulting solution aims to simplify the collection of data from systems, previously performed directly by system administrators, so that it can be performed by anyone who can physically approach the monitored system and thus reduce the costs associated with monitoring these remote systems.
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Enhancing Security Monitoring with AI-Enabled Log Collection and NLP Modules on a Unified Open Source Platform
Safonov, Yehor ; Zernovic, Michal
The number of computer attacks continues to increasedaily, posing significant challenges to modern securityadministrators to provide security in their organizations. Withthe rise of sophisticated cyber threats, it is becoming increasinglydifficult to detect and prevent attacks using traditional securitymeasures. As a result, security monitoring solutions such asSecurity Information and Event Management (SIEM) have becomea critical component of modern security infrastructures. However,these solutions still face limitations, and administrators areconstantly seeking ways to enhance their capabilities to effectivelyprotect their cyber units. This paper explores how advanced deeplearning techniques can help boost security monitoring capabilitiesby utilizing them throughout all stages of log processing. Thepresented platform has the potential to fundamentally transformand bring about a significant change in the field of securitymonitoring with advanced AI capabilities. The study includes adetailed comparison of modern log collection platforms, with thegoal of determining the most effective approach. The key benefitsof the proposed solution are its scalability and multipurposenature. The platform integrates an open source solution andallows the organization to connect any event log sources or theentire SIEM solution, normalize and filter data, and use thisdata to train and deploy different AI models to perform differentsecurity monitoring tasks more efficiently.
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Log management ELK stack
SLAVÍK, David
This work describes problems with computer logs and how to treat them. The main goal of this work is to show how to collect, transform, save, and explore logs created by an application or system. It presents the log management solution named ELK stack that consists of three main components Elasticsearch which is a NoSQL database and search engine, Kibana used as a graphical user interface for viewing, analyzing data also as creating dashboards. Logstash is a part of the stack that transforms and prepares the message structure for the Elasticsearch. An additional component is a beat used for collecting the data from servers. Part of this work also includes instructions on how to install, configure and create a workspace with data. The result of this work should be helpful for companies struggling with logs to implement their own log management solution.
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Performance Monitoring of MES PHARIS
Ondráček, Aleš ; Hruška, Martin (referee) ; Smrčka, Aleš (advisor)
This diploma thesis deals with the performance monitoring of automated development processes and performance testing of the MES PHARIS system. The main scope of thesis is the collection of data on tasks performed on automation servers DevOps and Jenkins, processing of this data and their subsequent visualization. The second part of the diploma thesis deals with the processing of data from performance testing and their appropriate representation using visualization. The core technology that is used is ELK Stack.
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Performance Data Collection of MES PHARIS
Oháňka, Martin ; Hruška, Martin (referee) ; Smrčka, Aleš (advisor)
This master's thesis deals with monitoring of automated tasks on integration servers and obtaining data from these tasks. Another area of this work is performance testing and to obtain information about hardware utilization from it. Thanks to this, it is possible to perform performance analysis of the implemented solution from different performance perspectives. The result of this master's thesis is a software solution that can obtain data about tasks from DevOps and Jenkins integration servers. In the area of performance testing, there is created a solution for parallel execution of tasks. The output of this work an output passed in JSON format. The data is then transferred to the Elastic platform, specifically Logstash, where it is subsequently visualized using Kibana. The Beat platform is used to collect data from performance testing. The solution was applied to the production information system MES PHARIS of the UNIS company.
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Log Analysis and Hardware Utilization
Kuchyňka, Jiří ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
The goal of this thesis is to design and implement a system for long-term monitoring of the state of Linux systems located in a production environment. The thesis focuses mainly on situation in which the system does not have the ability to send the collected data for analysis over the network, so data collection must be completely automatic and data must be transferred from monitored systems to a central system for collection, analysis and visualization. A substantial part of the work is devoted to the design and implementation of a web application used to export data from monitored systems to the transmission medium and import them from it to the system for data collection. The resulting solution aims to simplify the collection of data from systems, previously performed directly by system administrators, so that it can be performed by anyone who can physically approach the monitored system and thus reduce the costs associated with monitoring these remote systems.
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