Published Paper Details:

Protection of data in digital communication is important to avoid any tampering especially for information of sensitive nature like financial or personal details. The branch of mathematics known as cryptography is concerned with the processes of encryption and decryption which allows secure communication across open and untrustworthy channels. In this project, auto-associative neural networks based on Hebb's rule are proposed to advance cryptographic processes. The system issues keys for encryption, teaches neural networks to encode-decode data streams, and measures the effectiveness of the system in terms of accuracy and time factors. To prevent data from the growing range of threats, the frameworks formulates protection with neural network flexibility. The system also provides the user with a graphical display so that recording and playback is performed automatically. Such method indicates the prospect of use of neural networks for the improvement of ordinary codes and ciphers, and focuses a number of challenges emerging from the contemporary information security.