Enhancing Internet Data Security: A Fusion of Cryptography, Steganography, and Machine Learning Techniques
DOI:
https://doi.org/10.21123/bsj.2024.10371Keywords:
DNA Cryptography, Decryption, Machine Learning, Steganography, Security, Encryption, DNA Cryptography, Decryption, Machine Learning, Steganography, Security, EncryptionAbstract
Cryptography and steganography play critical roles in ensuring the security of network communications. Combining these methods holds great potential for safeguarding information transmitted over the internet. DNA Cryptography, a modern and robust technique, leverages the unique properties of DNA for secure data handling. The increasing of cyber threats has made it necessary to propose a new secure communication method to block unauthorized access to the sending information. This paper introduces an innovative system that integrates cryptography, steganography, and machine learning techniques to enhance data transfer security over the Internet. The system unfolds in six steps to encrypt and hide text. The initial step involves using the Caesar cipher to encode the original text. This is followed by the conversion of the text into DNA bases. Subsequent steps include the conversion of DNA bases to ASCII format and further transformation into binary numbers. The fifth step introduces a dynamic element by shifting binary numbers using a random key. The final step involves covertly embedding these binary numbers (ciphertext) within an image. Beyond traditional metrics, machine learning elements have been incorporated to elevate the system's performance. Performance evaluation was conducted across three standard images with varying data sizes, demonstrating the system's effectiveness. The proposed system showcased rapid cryptography times, with encryption and decryption times of 2.802 ms and 3.388 ms, respectively. The integration of machine learning techniques enriches the system's capabilities, presenting a compelling solution for secure and efficient data transfer over the Internet.
Received 04/12/2023
Revised 30/04/2024
Accepted 02/05/2024
Published Online First 20/09/2024
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Copyright (c) 2024 Omar Fitian Rashid , Mohammed Ahmed Subhi, Marwa Khudhur Hussein, Mohammed Najah Mahdi
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