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Distributed and Self-organizing Systems
Distributed and Self-organizing Systems

Masterarbeit / Bachelorarbeit

HTTP Message Classification for Content Trust Awareness
HTTP Message Classification for Content Trust Awareness

Research Area

Web Engineering





In the redecentralized web, wep applications have to make trust-aware decisions autonomously by definition of the redecentralization. The respective trust evaluation before each trust-aware decision must thus be also done by the web applications themself. Such a trust evaluation should further not only base upon the source of the data or the data provider, but it should be also content- and context-related to perceive the complexity of independent third parties' apps. A data source does not provide enough information to make a trust-aware decision, so additional factors, such as the content and context of an (HTTP) message could give a lot more inputs. The evaluation, and thus content classification as a part of it, cannot be done only once. It should be done for every message to support the high dynamic changes within the web, because the data of some recourse could be modified within time. Moreover, another application’s behaviour or content can also change without any notice due to the lack of a central authority monitoring it.

In the decentralized web, all HTTP applications require an autonomous way of considering message content regarding their own trust evaluation. However, there is no approach nowadays on how to get this knowledge in the context of trust evaluations automatically without any human activity. The very first information about any HTTP message could be found in the HTTP-header. It contains a data type from the message body. But even if the system is aware of the data type, it doesn't know anything about its content. To understand the content of the message, a system needs to classify the message. To understand the content of the message the information extraction process using NLP (Natural Language Processing) algorithms could be used. Such algorithms could provide the following information about the text: language, topic, general intention of the text, etc.

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