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The Multi Layer Auto Encoder Neural Network (ML-AENN) for Encryption and Decryption of Text Message

机译:用于加密和文本消息的多层自动编码器神经网络(ML-ANN)

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Efficient key generation techniques require highly secure cryptosystems. The traditional key generation technique is very systematic that it is easy to attack. The Deep Learning algorithm is one of the research paths into the automated extraction of complex data representations (features) at a high level of abstraction. Auto Encoder Neural Network (AENN), one of the deep learning's architectures, play an essential role in unsupervised learning in deep architecture for learning transfer and other tasks. The AENN are simple learning networks that aim to convert inputs into outputs with the least amount of distortion. This study proposes the Deep Learning approach for text message encryption and decryption by using the Multi-Layer AENN where the outputs of each of the deepest layer as the secret-key generator and hash value generator. The result of this research showed that the proposed method has a high degree of confidentiality because each training always produces a different secret key. Furthermore, the tampered data/information can be seen from the presence of different hash values that occur.
机译:有效的密钥生成技术需要高度安全的密码系统。传统的主要生成技术非常系统化,即易于攻击。深度学习算法是在高级抽象中自动提取复杂数据表示(特征)的研究路径之一。自动编码器神经网络(Aenn)是一个深度学习的架构之一,在深度架构中发挥了重要作用,用于学习转移和其他任务。 Aenn是简单的学习网络,旨在将输入转换为具有最小失真量的输出。本研究提出了通过使用多层Aenn的文本消息加密和解密的深度学习方法,其中每个最深层的输出作为秘密密钥发生器和散列值发生器。该研究的结果表明,该方法具有很高的机密性,因为每个训练总是产生不同的秘密密钥。此外,可以从发生的不同哈希值的存在中看到篡改数据/信息。

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