# AI Makes Crypto Evolve
## 7 AIEC
### Neural Cryptography
Furthermore, NNs consume a large amount of energy under the current von-neumman architecture due to the presence of a memory wall. In order to rectify this, Non-Volatile Memory (NVM) systems are seen as an alternative but they are susceptible to confidentiality attacks where the attacker can steal the NN model. With little performance overhead, a Sparse Fast Gradient Encryption (SFGE) method coupled with Runtime Encryption Scheduling (RES) can be used to secure the confidentiality of NNs. This can help mitigate the AI hardware-based attacks with a strong encryption scheme [201]. One method to leverage NNs in order to encrypt and decrypt text messages is the use of Auto Encoder Neural Networks (AENNs). An AENN is an FFNN which can be used for unsupervised learning where the input can also be the output. By leveraging the encryption scheme, key generation becomes extremely secure because for every training set it produces a different key. Therefore, for the same plaintext a different ciphertext is produced [56]. Real-time RNN-based ciphers can be employed to secure the transfer of mobile ad hoc networks through the use of a Multi-Path Routing Scheme. The steps involved in this process include message encryption through the use of RRNN symmetric cipher, message routing through multi-path DSR and message decryption with the RRNN-SC. This system helps address many of the underlying issues of confidentiality, integrity and access control currently experienced with Mobile Adhoc NETworks (MANETs). Looking at the Fast Handover protocol, which is usually in effect when a mobile router from a previous access point connects to a new one, it can be seen that it is susceptible to DOS and Masquerading attacks. In order to combat this, NNs can be used by leveraging their property of mutual learning. The Two parties communicating with each other use a TPM and the key generated is the synchronized weights of the two entities. This provides a better communication system than the proposed solutions of using PKI or Diffie-Helman because of their slowness and lack of efficiency [202]. An HNN encryption scheme can be used to encrypt text-messages. Then, in order to bolster security, a DNA cryptography model can be used [149]. Security of Neural Cryptography  
Interestingly, side channel attacks can be curbed through the use of Tree Parity Machine Public Key Exchanges (TPMPKE) [203]. Neural Synchronization looks at the communication of two TPMs to establish a secret key via an open channel. Once this is successfully established, it is highly resistant to the majority flipping attack by a cooperating attacker. The security of neural cryptography can be compromised if there are multiple cooperators that work together to decrypt the exchange key using their own NNs [185]. Symmetric ciphers based on real-time RNNs can be subject to Chosen Plaintext Attacks (CPA). If the adversary has access to arbitrary amounts of plaintext and can generate the ciphertext for them: the resulting input and output can be fed into an NN and the key can be discovered [204]. Neural Cryptography protocols can be attacked using a technique known as 'power analysis'. This protocol is weak against this attack where the power can trace the information which is exploited [205]. A counter measure is sending Erroneous information which can be transmitted in order to deceive the attacker listening to the communication about the content of the information.