Due to the potential security problem about key management and distribution for the symmetric image encryption schemes, a novel asymmetric image encryption method is proposed in this paper, which is based on the elliptic curve ElGamal (EC-ElGamal) cryptography and chaotic theory. Specifically, the SHA-512 hash is first adopted to generate the initial values of a chaotic system, and a crossover permutation in terms of chaotic index sequence is used to scramble the plain-image. Furthermore, the generated scrambled image is embedded into the elliptic curve for the encrypted by EC-ElGamal which can not only improve the security but also can help solve the key management problems. Finally, the diffusion combined chaos game with DNA sequence is executed to get the cipher image. The experimental analysis and performance comparisons demonstrate that the proposed method has high security, good efficiency, and strong robustness against the chosen-plaintext attack which make it have potential applications for the image secure communications. INDEX TERMS SHA-512 hash, elliptic curve ElGamal encryption, chaos game, crossover permutation.

In this paper, a color image encryption method using the memristive hyperchaotic system and deoxyribonucleic acid (DNA) encryption is proposed. First, the pseudo-random sequences are generated by a keystream generation mechanism based on a memristive hyperchaotic system and the plain image. Due to this, the memristive hyperchaotic system has a complex dynamical behavior and is highly sensitive to initial conditions, the proposed keystream generation mechanism is highly random which is also dependent on the plain images. Second, a permutation based on the cycle-shift operation is designed to eliminate the correlations between adjacent pixels in the plain images. Then, the scrambled sequences are processed by DNA encryption to increase the system ability to defense the brute force attacks. Finally, the cipher image is obtained after the diffusion and interaction among red, green and blue components. Experimental analysis and performance comparisons show that the proposed method has high security, good efficiency and strong robustness under different attacks.

Infrastructure-as-a-Service cloud platforms are incredibly complex: they rent hundreds of different types of servers across multiple geographical regions under a wide range of contract types that offer varying tradeoffs between risk and cost. Unfortunately, the internal dynamics of cloud platforms are opaque in several dimensions. For example, while the risk of servers not being available when requested is critical in optimizing these risk-cost tradeoffs, it is not typically made visible to users. Thus, inspired by prior work on Internet bandwidth probing, we propose actively probing cloud platforms to explicitly learn such information, where each "probe" is a request for a particular type of server. We model the relationships between different contracts types to develop a market-based probing policy, which leverages the insight that real-time prices in cloud spot markets loosely correlate with the supply (and availability) of fixed-price on-demand servers. That is, the higher the spot price for v a server, the more likely the corresponding fixed-price on-demand server is not available. We incorporate market-based probing into SpotLight, an information service that enables cloud applications to query this and other data, and use it to monitor the availability of more than 4500 distinct server types across 9 geographical regions in Amazon's Elastic Compute Cloud over a 3 month period. We analyze this data to reveal interesting observations about the platform's internal dynamics. We then show how SpotLight enables two recently proposed derivative cloud services to select a better mix of servers to host applications, which improves their availability from

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