Abstract:Recently, chaotic dynamics-based data encryption techniques for wired and wireless networks have become a topic of active research in computer science and network security such as robotic systems, encryption, and communication. The main aim of deploying a chaos-based cryptosystem is to provide encryption with several advantages over traditional encryption algorithms such as high security, speed, and reasonable computational overheads and computational power requirements. These challenges have motivated researchers to explore novel chaos-based data encryption techniques with digital logics dealing with hiding information for fast secure communication networks. This work provides an overview of how traditional data encryption techniques are revised and improved to achieve good performance in a secure communication network environment. A comprehensive survey of existing chaos-based data encryption techniques and their application areas are presented. The OPEN ACCESSEntropy 2015, 17 1388 comparative tables can be used as a guideline to select an encryption technique suitable for the application at hand. Based on the limitations of the existing techniques, an adaptive chaos based data encryption framework of secure communication for future research is proposed.

In this paper a number of image encryption algorithms based on chaotic maps has been proposed. Images are routinely used in diverse areas such as medical, military, science, engineering, art, entertainment, advertising, education as well as training. The fundamental issue of protecting the confidentiality, integrity as well as the authenticity of images has become a major concern. Most of the available encryption algorithms are used for text data. However, due to large data size and real time requirement, the algorithms that are appropriate for textual data may not be suitable for multimedia data. Some cryptographic algorithms such as RSA, DES and AES are not sufficient for image encryption. We try to implement Image encryption using S-DES (Simplified Data Encryption Standard). In preceding work, most researchers used to make a image using a key and then encrypt the chaotic image using the same key, but in this paper first make a chaotic map of the image using S-DES. Then use that chaotic image as a key for encrypting the image using S-DES. Thus in this paper select the key when encrypt the image and use a chaotic image as a key not any other text. Thus the encryption speed is some faster in this implementation as compare to previous work. S-DES is the reduced algorithm of DES. DES uses a well-known block cipher; it adopts Fiestel structure to iterate. The key Quantities achieve 56 bits, using the only key in an encryption is not safe obviously. Therefore a new approach has been proposed named as S-DES, which also adopts Fiestel structure. Combining the chaotic map with S-DES system can enhance the security of system by using the characteristic of sensibility of original value and randomness in chaotic map. Thus the encryption speed is fast in this implementation as compare to previous work.

Cloud computing is a type of grid computing which is a form of distributed computing and distributed computing is a special type of parallel computing. Presently a lot of services are growing under the single umbrella that is known as cloud computing. Cloud computing gain popularity in the several area due its property of everything-as-a-service(XaaS), includes SaaS, PaaS and IaaS. Many problems have been arising when we go for implementation development. Workflow scheduling and appropriate allocation of resources is one of among problems that will decrease the Quality of Service (QoS) of cloud computing. There are many algorithms to automate the workflows in a way to satisfy the Quality of service (QoS) of the user. This paper is the survey of some workflow scheduling algorithms that have been proposed for cloud computing.

We suggest a shading essentially based division theory using the Convolution Neural Network technique to observe tumor protests in cerebrum pictures of reverberation (MR). During this shading, the mainly based algorithmic division guideline with FCNN suggests that changing over a given dark level man picture into a shading territorial picture at that point separates the situation of tumor objects from partner man picture elective objects by fully exploiting Convolution Neural Network and bar outline package. Analysis shows that the methodology will succeed in dividing human mind images to help pathologists explicitly recognize the size and district of size.

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