Neural synchronization based secret key exchange over public channels: A survey <s> V. TYPES OF ATTACKS <s> A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations. <s> BIB001 </s> Neural synchronization based secret key exchange over public channels: A survey <s> V. TYPES OF ATTACKS <s> Neural cryptography deals with the problem of “key exchange” between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process. Therefore, diminishing the probability of such a threat improves the reliability of exchanging the output bits through a public channel. The synchronization with feedback algorithm is one of the existing algorithms that enhances the security of neural cryptography. This paper proposes three new algorithms to enhance the mutual learning process. They mainly depend on disrupting the attacker confidence in the exchanged outputs and input patterns during training. The first algorithm is called “Do not Trust My Partner” (DTMP), which relies on one party sending erroneous output bits, with the other party being capable of predicting and correcting this error. The second algorithm is called “Synchronization with Common Secret Feedback” (SCSFB), where inputs are kept partially secret and the attacker has to train its network on input patterns that are different from the training sets used by the communicating parties. The third algorithm is a hybrid technique combining the features of the DTMP and SCSFB. The proposed approaches are shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize. <s> BIB002
Public channels exchanging secret keys through neural synchronization may experience primarily four kinds of attacks: simple, geometric, majority and genetic BIB002 . In simple attack, the attacker's neural network has the same structure as that of the end point networks. All that the attackers have to do is to start with random initial weights and to train with the same inputs transmitted between the end point networks. A geometric attack, on the other hand, can outperform a simple attack, because, in addition to applying the same learning process, the attacker can utilize the output of the attacker machine and the local fields of its hidden units. The majority attack or the cooperative attack is similar to the geometric attack, but here the attacker can increase the probability to predict the internal representation of any of the partners' neural network, by using several networks, with random weight vectors, working in a cooperating group rather than as individuals. In the genetic attack, the attacker starts with only one network but is permitted to use more networks as synchronization progresses. In 2004, Shacham et al. have shown that a group of attackers cooperating amongst themselves (i.e. cooperative attack) throughout the synchronization process can have high degrees of success in breaking the neural synchronization based key exchange BIB001 .