Article
ENCRYPTION AND DECRYPTION ALGORITHM BASED ON NEURAL NETWORK
With the rapid growth of digital communication, cloud computing, and the Internet of Things (IoT), protecting sensitive information has become increasingly important. Conventional cryptographic algorithms such as AES, RSA, and ECC provide strong security but face challenges related to computational complexity, key management, and adaptability against evolving cyber threats. Recent advances in Artificial Intelligence (AI), particularly Neural Networks (NNs), have opened new opportunities for developing intelligent cryptographic systems capable of learning complex data transformations. This research proposes a Neural Network-Based Encryption and Decryption Algorithm that utilizes deep learning techniques to generate secure encryption mappings while maintaining accurate decryption capabilities. The proposed model employs a multilayer neural network trained on plaintext-ciphertext pairs to learn nonlinear encryption functions. The system incorporates dynamic key generation, neural weight optimization, and secure decoding mechanisms to improve confidentiality and resistance against statistical, brute-force, and differential attacks. The proposed methodology includes data preprocessing, neural network training, encryption using learned weights, and decryption through inverse neural mapping. Performance is evaluated using encryption time, decryption accuracy, entropy, avalanche effect, key sensitivity, and resistance against cryptographic attacks. Experimental results demonstrate that the neural network-based approach provides enhanced security, adaptability, and scalability while maintaining competitive computational efficiency compared to traditional encryption techniques.
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