i want m.phil thesis titles, cryptography and network security

Neural Networks are well known for their ability to selectively explore the solution space of a given problem. This feature finds a natural niche of application in the field of cryptanalysis. At the same time, Neural Networks offer a new approach to attack ciphering algorithms based on the principle that any function could be reproduced by a neural network, which is a powerful proven computational tool that can be used to find the inverse-function of any cryptographic algorithm.

The ideas of mutual learning, self learning, and stochastic behavior of neural networks and similar algorithms can be used for different aspects of cryptography, like public-key cryptography, solving the key distribution problem using neural network mutual synchronization, hashing or generation of pseudo-random numbers.

Another idea is the ability of a neural network to separate space in non-linear pieces using "bias". It gives different probabilities of activating or not the neural network. This is very useful in the case of Cryptanalysis.

Two names are used to design the same domain of researches : Neuro-Cryptography and Neural Cryptography.

The first work that it is known on this topic can be traced back to 1995 in an IT Master Thesis.There are currently no practical applications due to the recent development of the field, but it could be used specifically where the keys are continually generated and the system (both pairs and the insecure media) is in a continuously evolving mode.
In 1995, Sebastien Dourlens applied neural networks cryptanalyze DES by allowing the networks to learn how to invert the S-tables of the DES. The bias in DES studied through Differential Cryptanalysis by Adi Shamir is highlighted. The experiment shows about 50% of the key bits can be found, allowing the complete key to be found in a short time. Hardware application with multi micro-controllers have been proposed due to the easy implementation of multilayer neural networks in hardware.
One example of a public-key protocol is given by Khalil Shihab. He describes the decryption scheme and the public key creation that are based on a backpropagation neural network. The encryption scheme and the private key creation process are based on Boolean algebra. This technique has the advantage of small time and memory complexities. A disadvantage is the property of backpropagation algorithms: because of huge training sets, the learning phase of a neural network is very long. Therefore, the use of this protocol is only theoretical so far.