Baqer Mahdi Baqer Merzah

In recent years, technology advanced rapidly. Therefore, ensuring the security of information transmitted over the Internet is necessary. To increase data security and confidentiality, different encryption algorithms were developed. In this study, to enhance security, a new symmetric key encryption algorithm is proposed by generating key blocks based on random numbers sent from a sender to a receiver. For encryption, the XOR operation is performed on the surrounding elements of the plaintext block with the key block and on the middle elements of the plaintext block with the new elements generated after the previous XOR operation. The proposed encryption technique was evaluated and the results show the efficiency of the proposed technology in generating keys. The proposed technique tested on different data sizes and results obtained that show the effectiveness and efficiency in the time of encryption and decryption.

The purpose of this work is to investigate the problem of detecting transportable borrowings and text reuse. The article proposes a monolingual solution to this problem: translating the suspicious material into language collections for additional monolingual analysis. One of the major requirements for the suggested technique is robustness against machine learning ambiguities. The next step in the document analysis is split into two parts. The authors begin by retrieving documents-candidates that are similarity to other types of text recurrence. The paper proposes retrieving texts utilizing word clusters formed using distributional semantic for robustness. In the second stage, the authors use deep learning neural networks to compare the suspected document to candidates utilizing phrase embedding. The experimentation is carried out for the language pair “English-Arabic” on both articles and synthetic data.