# AI Makes Crypto Evolve
## 7 AIEC
### Neural Cryptography
Clipped Hopfield Neural Network (CHNN) and multivariate cryptography can also be used for a public key cryptography system [172]. Symmetric key cryptography can use genetic algorithms and an Error back propogration neural network to encrypt and decrypt data. The encryption process uses the GA while the decryption process uses EP-NN which is a commonly used for supervised learning NNs. The time complexity for this algorithm is significantly less than that of Advanced Encryption Standard (AES), Data Encryption Standard (DES) and RSA [173]. The chaos property of ANNs can also be used to encrypt digital video transmission. One encryption algorithm from chaotic NN uses MPEG-2 video codec standard to change the audio-visual video data into an encrypted form [174; 175]. The encrypted data are then transmitted to the destination using the OFDM modulation technique. The creation of a cipher based on the chaotic NN property reveals an interesting characteristic where the control parameter must be close to 4 [174]. Otherwise, the cipher system does not possess sufficient chaotic properties which can lead to crypt-analytic vulnerability. Another way this can be achieved is through the binary sequence that is generated via the chaotic system along with the biases and weights that are set. This, in combination with the VLSI architecture, can create an encryption method which has the following characteristics: high security, limited distortion and suitability for digital integration. The noise-like chaotic properties of ANN can also be used for signal encryption using time-series generation and hash-value generation. These can be fused with the ANN and the chaotic sequences to create a signal digital envelope which has a secret key and a hash value associated with it [176; 177]. Delayed NNs can be applied in the master-slave synchronisation field. Specifically, they can be deployed where network and controller modes are asynchronous and a memory asynchronous control allows for a solution to the outer synchronization problem. The verifiability of this is seen through chaotic-like NNs and their application to encryption [178]. Memristor-Based BAM Neural Networks are widely used in synchronization system. They can also be applied to encryption processes because of their chaotic property [179]. With the efficiency of key generation and high development ability of NNs, many communication systems use NNs to achieve secure communication networks. An NN can provide system synchronization and the encryption process [180]. Furthermore, it can be made more secure by combining a hyper-chaotic function with the NN. A hyper-chaotic system would require at least 4 Di chaotic functions, which the NN can quickly obtain. The encryption process from the hyper-chaotic NN would provide even more secure communication. Impulsive synchronization can be achieved using two reaction-diffusion NNs [181]. The networks can be combined with the Lyapunov function to ensure less conservative criteria compared to the regular impulsive synchronization system. Similarly to other NNs in synchronization, reaction-diffusion NNs can also provide encryption and decryption while synchronizing computers. We mentioned the Memristor-Based Neural Networks above, which can be used for synchronization systems. Now, they can become be more powerful as they can be improved for finite-time synchronization [182]. They can perform synchronization with time-varying delays and distributed delays to guarantee the synchronization time in a finite time. The delays algorithm would be integrated into the NN to perform synchronization and encryption. Traditional time-triggered communication systems with data-sampled control would send additional sampled data that are unnecessary. The event-triggered communication system can prevent the problem of wasting limited bandwidth due to its properties [183]. It only transmits data when the event condition is met.