Neural synchronization based secret key exchange over public channels: A survey <s> VIII. OTHER RECENT STUDIES <s> Side-channel attacks have changed the design of secure cryptographic systems dramatically. Several published attacks on implementations of well known algorithms such as, e.g., AES, show the need to consider these aspects to build more resistant cryptographic systems. On the other hand, with the increasing use of cryptography in embedded systems a significant demand exists for cryptographic algorithms that are both resource-and power-efficient. These can be either modified existing or completely new ones. One of the candidates for such a new algorithm is the Tree Parity Machine Public Key Exchange, an algorithm based on artificial neural networks. While it has been evaluated in a number of practical applications in the past, its side-channel resistance has not been examined yet. We would like to close this gap and present a side-channel attack strategy as well as results gathered from measurements made on a real implementation. <s> BIB001 </s> Neural synchronization based secret key exchange over public channels: A survey <s> VIII. OTHER RECENT STUDIES <s> Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied. <s> BIB002 </s> Neural synchronization based secret key exchange over public channels: A survey <s> VIII. OTHER RECENT STUDIES <s> An algorithm is presented which implements a probabilistic attack on the key-exchange protocol based on permutation parity machines. Instead of imitating the synchronization of the communicating partners, the strategy consists of a Monte Carlo method to sample the space of possible weights during inner rounds and an analytic approach to convey the extracted information from one outer round to the next one. The results show that the protocol under attack fails to synchronize faster than an eavesdropper using this algorithm. <s> BIB003 </s> Neural synchronization based secret key exchange over public channels: A survey <s> VIII. OTHER RECENT STUDIES <s> In this paper, a neural network guided secret key based technique for encryption (ANNRPMS) has been proposed using recursive positional modulo-2 substitution for online transmission of data/information through wireless communication. In this approach both the communicating networks receive an indistinguishable input vector, produce an output bit and are trained based on the output bit. The dynamics of the two networks and their weight vectors are found to a novel experience, where the demonstrate networks synchronize to an identical time dependent weight vectors. This observable fact has been used to form a secured variable length secret-key using a public channel. The length of the key depends on the number of input and output neurons. The original plain text is encrypted using recursive positional modulo-2 substitution technique. The secret key also fabricated through encryption process in a cascaded manner. This intermediate cipher text is again encrypted to form the final cipher text through chaining and cascaded xoring of identical weight vector with the identical length intermediate cipher text block. If size of the last block of intermediate cipher text is less than the size of the key then this block is kept unchanged. Receiver will use identical weight vector for performing deciphering process for getting the recursive positional modulo-2 substitution encrypted cipher text and secret key for decoding. A session key based transmission has also been proposed using 161-bit key format of 14 different segments [7,8]. Parametric tests are done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed system. <s> BIB004 </s> Neural synchronization based secret key exchange over public channels: A survey <s> VIII. OTHER RECENT STUDIES <s> Key exchange is one of the major concerns in cryptology. Mutual learning between two neural networks has been used to address this issue. However, there is still a need to extend the concept so that a group of parties can share a common key. This work suggests an algorithm that employs and extends the mutual learning process to accommodate the much needed group secure communication. Here, a recursive algorithm that depends on ordering the parties in a binary tree structure is introduced. By applying this algorithm, a group of N parties is able to share a common key in a time proportional to sup(log 2 (N)). The proposed algorithm implements two binary tree approaches to achieve this objective, one uses a binary tree with swapping, BTWS, while the other employs a binary tree with election, BTWE. <s> BIB005 </s> Neural synchronization based secret key exchange over public channels: A survey <s> VIII. OTHER RECENT STUDIES <s> Neural synchronization by means of mutual learning provides an avenue to design public key exchange protocols, bringing about what is known as neural cryptography. Two identically structured neural networks learn from each other and reach full synchronization eventually. The full synchronization enables two networks to have the same weight, which can be used as a secret key for many subsequent cryptographic purposes. It is striking to observe that after the first decade of neural cryptography, the tree parity machine (TPM) network with hidden unit K = 3 appears to be the sole network that is suitable for a neural protocol. No convincingly secure neural protocol is well designed by using other network structures despite considerable research efforts. With the goal of overcoming the limitations of a suitable network structure, in this paper we develop a two-layer tree-connected feed-forward neural network (TTFNN) model for a neural protocol. The TTFNN model captures the notion that two partners are capable of exchanging a vector with multiple bits in each time step. An in-depth study of the dynamic process of TTFNN-based protocols is then undertaken, based upon which a feasible condition is theoretically obtained to seek applicable protocols. Afterward, according to two analytically derived heuristic rules, a complete methodology for designing feasible TTFNN-based protocols is elaborated. A variety of feasible neural protocols are constructed, which exhibit the effectiveness and benefits of the proposed model. With another look from the perspective of application, TTFNN-based instances, which can outperform the conventional TPM-based protocol with respect to synchronization speed, are also experimentally confirmed. <s> BIB006 </s> Neural synchronization based secret key exchange over public channels: A survey <s> VIII. OTHER RECENT STUDIES <s> Neural cryptography, a type of public key exchange protocol, is widely considered as an effective method for sharing a common secret key between two neural networks on public channels. How to design neural cryptography remains a great challenge. In this paper, in order to provide an approach to solve this challenge, a generalized network architecture and a significant heuristic rule are designed. The proposed generic framework is named as tree state classification machine (TSCM), which extends and unifies the existing structures, i.e., tree parity machine (TPM) and tree committee machine (TCM). Furthermore, we carefully study and find that the heuristic rule can improve the security of TSCM-based neural cryptography. Therefore, TSCM and the heuristic rule can guide us to designing a great deal of effective neural cryptography candidates, in which it is possible to achieve the more secure instances. Significantly, in the light of TSCM and the heuristic rule, we further expound that our designed neural cryptography outperforms TPM (the most secure model at present) on security. Finally, a series of numerical simulation experiments are provided to verify validity and applicability of our results. <s> BIB007
Side channel attacks on neural synchronization models of key exchange have also been evaluated. In a paper during the same time when the above algorithms got published, the authors investigated schemes of side channel attacks and their effects on communicating channels with tree parity machine models of neural networks at both end points BIB001 . To test the validity of the neural synchronization based key exchange algorithms on models other than the TPM, a key exchange algorithm was also proposed based on multilayer feed-forward neural networks incorporating permutation parity machines BIB002 . This algorithm's performance against the common attacks on such channels e.g. simple, geometric, majority and genetic was exhaustively studied and the performance (based on various metrics) was found quite satisfactory. Other proposals also tested the utility of the PPMs for key exchange BIB003 24] . Neural cryptography using a form of information substitution was also proposed in 2011 BIB004 . In this approach recursive positional modulo-2 substitution, both the communicating networks received an indistinguishable input vector, and produced an output bit. Then these networks were trained based on these output bits. Based on this scheme, a secret-key, of variable length, was formed. The original information, which was represented in plain text format, was encrypted using the substitution technique, i.e. recursive positional modulo-2 substitution. The intermediate-level cipher information was again encrypted to form the final cipher text. This was done through chaining and cascaded xoring of identical weight vectors with the identical length intermediate cipher text block. The receiver then used identical weight vector for performing deciphering process for getting the encrypted cipher text and secret key for decoding. This particular method proved to be an inspiration for some later exploratory work on neural synchronization based variable length keys for encrypting symbolic information. In addition to this, an important study has also been performed for group key exchange schemes. Here, a group of end point parties can share a common key. A recursive algorithm was proposed, where it was shown that a group of N parties is able to share a common key in an acceptable time (sup (logi N)) BIB005 . This acceptable time complexity primarily depended on the topological arrangement of the parties in the group withrespect to each other. In this particular work, the end point communicating parties were ordered on variations of binary tree topologies, e.g. swapping and election. But in 2012, an algorithm was proposed which demonstrated that successful attacks are quite possible on permutation-parity-machine-based neural cryptosystems. This particular algorithm executed a probabilistic form of an attack on the key-exchange protocol BIB003 . Previously such algorithms/schemes of attacks that worked by imitating the synchronization of the communicating partners had been proposed (discussed previously in this paper). But here, a Monte Carlo method was used to sample the space of possible weights during inner rounds of learning. Thereafter, the sampled inner round information was conveyed from one outer round of learning to the next one. It was shown in this work that the mutual learning based protocol failed to synchronize faster than an attacker who used this sampling based inner and outer round learning. This paper has been recognized as opening up of new areas of investigation in neural cryptography, esp. for designing faster synchronizing protocols between the end point neural networks. In the current year (2013), another work also analysed the uti lity and degree of success of chaotic and Sequential Machine models of neural network synchronization  . As research in this field progressed, a general consensus started emerging that, till now, the tree parity machine (TPM) network with hidden unit K=3 is the model that is most suitable for a neural protocol BIB006 . Thus, it becomes important to find more variety of neural network architectures and synchronizing mechanisms that provide enhanced security to different forms of attacks. Thus, in 2013, a two-layer tree-connected feed-forward neural network (TTFN N) model was proposed. This model utilized the concept that two communicating partners are capable of exchanging a vector with multiple bits in each time step BIB006 . In this work, feasible conditions and heuristic rules that would make the neural synchronization based protocol successful against common attacks were obtained. The authors proposed methods for developing TTFN N based schemes, and discovered cases of that have better synchronization speed than some of the previously published protocols based on the established TPM model of the communicating partners. In another analysis, the security of neural cryptography systems was studied by investigating the dynamics of information leakage through the learning process  . This study also analysed how the leaked information can be used to reduce the complexity of common attack strategies. In a different but seminal study very recently, a generalized architecture of the end point neural networks, actually extended and unified models of tree parity machine (TPM) and tree committee machine (TCM), were proposed BIB007 . In addition to this, the authors also proposed a heuristic rule for such generalized architectures. The algorithm incorporating the generalized architectures seemed to have better performance than the protocols using the Tree Parity Machine models of the end point networks (like some of the previously discussed ones). In this proposed generic framework the authors also found that the heuristic rule can actually improve the security to common attacks to their scheme. Thus, we have discussed and surveyed the most important contributions in the recent past on neural synchronization based key exchange, and also the algorithms and results that have defined this field.