Diplomarbeit, 2015
111 Seiten, Note: 1
This thesis aims to explore the application of process mining techniques and algorithms to network protocols. It investigates how process mining, a discipline that bridges computational intelligence, data mining, process modeling, and analysis, can be leveraged to understand and enhance network protocol behavior. The work focuses on analyzing event data generated by network protocols, extracting insights into their control flow, and exploring the potential for optimizing their performance and security.
This work focuses on process mining, network protocols, event data analysis, algorithm evaluation, notation systems, TCP, HTTP, and protocol reverse engineering. It explores the application of process mining techniques and algorithms for discovering, analyzing, and enhancing the behavior of network protocols, using event data as the primary source of information. The research investigates the practical implementation of process mining for network protocols, including data preparation, algorithm selection, model generation, and potential extensions to other protocols and applications.
Process mining is a research discipline that links data mining and process modeling to discover, check, and enhance processes based on event data logged by systems like network protocols.
By analyzing event data from TCP, process mining can discover the actual control flow, check for conformance against standards, and identify bottlenecks or deviations to optimize performance.
The three main types are Discovery (creating a model from logs), Conformance (checking if logs match a model), and Enhancement (improving an existing model using log data).
ETL stands for Extract, Transform, and Load. It is the process of extracting raw network data, transforming it into a format suitable for mining, and loading it into tools like Disco or RapidMiner.
Yes, the research demonstrates that process mining techniques can be applied to understand and reconstruct the behavior of unknown or alternative protocols through event data analysis.
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