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Integration of advanced artificial intelligence methods with log management security systems
Sedláček, Jiří ; Mikulec, Marek (referee) ; Safonov, Yehor (advisor)
Cyber security is a very important aspect of everyone’s daily life. With the ever-expanding cyberspace and its growing influence on the real world, the issue of cyber security is all the more important. The theoretical part of the thesis describes the basic aspects of security monitoring. Also, the process of collecting event logs and their management is briefly described. An important means of security monitoring is the management of security information and events. Its advantages, disadvantages and possible improvements with artificial intelligence are discussed. Security orchestration, automation and response functions are also mentioned in the theoretical part. Machine learning techniques such as neural networks and deep learning are also mentioned. This section also focuses on cyber operations centres in terms of improving the efficiency of human ”manual” labour. A survey of possible machine learning techniques for this use case has been conducted, as the lack of human resources is a critical issue within security operations centres. The practical part of the thesis involves setting out a goal (text sequence classification) that could make the work considerably easier in terms of manually categorizing event logs according to their source. For this set task, security monitoring related data was collected from different log sources. In the practical part, the methods for processing this data are also described in detail. Subsequently, a suitable neural network model was selected and its technical description was performed. Finally, the final data processing and the process of training, validating and testing the model are described. Three scenarios were developed for this process, which are then described in detail in the measurement results.
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Integration of advanced artificial intelligence methods with log management security systems
Sedláček, Jiří ; Mikulec, Marek (referee) ; Safonov, Yehor (advisor)
Cyber security is a very important aspect of everyone’s daily life. With the ever-expanding cyberspace and its growing influence on the real world, the issue of cyber security is all the more important. The theoretical part of the thesis describes the basic aspects of security monitoring. Also, the process of collecting event logs and their management is briefly described. An important means of security monitoring is the management of security information and events. Its advantages, disadvantages and possible improvements with artificial intelligence are discussed. Security orchestration, automation and response functions are also mentioned in the theoretical part. Machine learning techniques such as neural networks and deep learning are also mentioned. This section also focuses on cyber operations centres in terms of improving the efficiency of human ”manual” labour. A survey of possible machine learning techniques for this use case has been conducted, as the lack of human resources is a critical issue within security operations centres. The practical part of the thesis involves setting out a goal (text sequence classification) that could make the work considerably easier in terms of manually categorizing event logs according to their source. For this set task, security monitoring related data was collected from different log sources. In the practical part, the methods for processing this data are also described in detail. Subsequently, a suitable neural network model was selected and its technical description was performed. Finally, the final data processing and the process of training, validating and testing the model are described. Three scenarios were developed for this process, which are then described in detail in the measurement results.
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