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Abstract

Financial and commercial institutions increasingly rely on Extensible Markup Language (XML) files as a standard means of exchanging data. However, this extensive use has created serious security challenges due to the fact that these files contain sensitive information such as bank card numbers and expiration dates. Relying on traditional full file encryption methods achieves a high degree of security, but it causes problems related to the large file sizes that consume memory and the long encryption and decryption times, which reduces the efficiency of systems when dealing with a large number of daily transactions. Methods based on Type-1 Fuzzy Logic System (T1FLS) have failed to address the ambiguity and uncertainty inherent in data, which can lead to the misclassification of some sensitive elements. Based on this gap, this research proposes an intelligent model based on Type-2 Fuzzy Logic System (T2FLS) to classify XML file components and determine their security importance more accurately. Partial encryption can then be applied only to the parts classified as highly sensitive. The proposed model was evaluated through a series of experiments on financial files in XML formats. The results showed that it reduced encryption time by 49% compared to full encryption, reduced the size of the encrypted file by approximately 60%, and reduced memory consumption by 32%. The model also demonstrated strong resistance to common security attacks. This research confirms that combining type-2 fuzzy logic with standard cryptographic techniques achieves a balance between efficiency and security, making it suitable for application in banking and e-commerce systems.

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