# The Dichotomy of Neural Networks and Cryptography: War and Peace
## 4 Peace: Coexistence and Alliance
### Alliance
#### 4.2.1 The Role of NNs in Cryptography
This can pose an issue since the security of the model relies then on the RSA key and not the Neural Cryptography system [99]. To resolve this issue a NN at the receiver end and a stochastic encryption method at the senders end can be used to eliminate the need for key exchanges all together [99]. Finally, Wavelet Chaotic NNs (WCNN) and chaotic NNs have also been used for secure encryption and decryption of images [100]. However, research has shown that WCNN provides stronger ciphertext [100]. Furthermore,during transmission the only data that would need to be sent is approximation coefficients, reducing the size of the ciphertext drastically [100]. * **Video:** For video encryption of the MPEG-2 video code, research has shown that using Chaotic NNs to encrypt the bitstream results in high entropy and high key sensitivity, both desirable results for security [101]. This model transmits data via the Orthogonal Frequency Division Multiplexing (OFDM) modulation technique and controls the bit rate and quality of the decryptedvideo [101]. A hybrid chaos and NN cipher encryption algorithm for compressed video signal transmission over wireless channel. * **Text:** While image and video encryption introduces new types of encryption, some approaches taken for text encryption have been to improve upon existing systems to provide new cryptographic schemes. Some groups have used Neural cryptography to encrypt plaintext, generating both a secret key and a hash using the Auto Encoder NNs (AENN) [102]. AENN is a NN meant to provide the least possible distortion to the resulting ciphertext, this allows ciphertext normalization to still appear as ascii [102]. Another improvement of schemes is the use of secret dimensions of a NN model as key instead of relying on asymmetric keys and trapdoor functions [103]. The application of delayed Chaotic NNs to generate binary sequences has also been researched in text encryption [104]. The binary sequence is used to create the key for the first level of encryption [104]. Then used in conjunction with DNA cryptography to create a secure ciphertext [104]. * **Applications in Security-Related Scenarios:*
* There are some security-related scenarios, which depend on cryptography. Neural networks have been used by researchers in many of these scenarios. To mention a few, we may refer to the following. * The security of ubiquitous computing has seen great improvement due to Neural cryptography. The idea of neural synchronization to generate shared keys is currently one that provides real-time security for systems already in place [105]. * While issues with WiMAX have been thoroughly documented [106]. Neural Cryptography proposes solutions to authentication and authorization by creating neural synchronized key pairs [106]. To achieve this neural synchronization two NNs are created with the same weight changing algorithm and passed the same input [107]. To achieve neural synchronization boundary conditions are set, whenever both weights shift to the same direction and one of the networks touches the boundary, the boundaries close tighter eventually leading to neural synchronization [107]. RFID has seen many problems due to having no international standards and posses security risk, one proposed solution [108]. Involves using a tree parity NN in order to perform key generation [108]. Biometric recognition for authentication has also seen support from deep recurrent NNs in order to increase accuracy and performance of models [109]. * Steongraphy is the study of hiding messages within something that is not a message, in some cases an image. One way to achieve this using neural cryptography is to first perform Discrete Cosine similarity Transform and Elliptic Curve Cryptography to first encrypt the image you would like to hide [110]. Then using a Deep NN this message is embedded into a host image [110]. Other groups have achieved similar results of image using Self-Organizing Map (SMO) NNs with 26 clusters for every letter of the alphabet [111].