In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Image encryption techniques play a significant role in multimedia applications to secure and authenticate digital images. This paper presents a comprehensive study of various image encryption techniques. This paper covers the most significant developments in meta-heuristic based image encryption techniques. The various attacks and performance measures related to image encryption techniques have also been studied. The existing techniques are analyzed with respect to differential, statistical, and key analyses. The main goal of this paper is to give a broad perspective on characteristics of image encryption techniques. The paper concludes by discussing significant advancements in the field of image encryption and highlighting future challenges.

A Comprehensive Review on Image Encryption Techniques

Various real-world multiobjective optimization problems are dynamic, requiring evolutionary algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization problem once an environmental change occurs. To this end, several methods have been developed to predict the new location of the moving Pareto set (PS) so that the population can be reinitialized around the predicted location. In this paper, we present a multidirectional prediction strategy to enhance the performance of EAs in solving a dynamic multiobjective optimization problem (DMOP). To more accurately predict the moving location of the PS, the population is clustered into a number of representative groups by a proposed classification strategy, where the number of clusters is adapted according to the intensity of the environmental change. To examine the performance of the developed algorithm, the proposed prediction strategy is compared with four state-of-the-art prediction methods under the framework of particle swarm optimization as well as five popular EAs for dynamic multiobjective optimization. Our experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs.

Multidirectional Prediction Approach for Dynamic Multiobjective Optimization Problems

Resource provision algorithms in cloud computing

The Muskingum model is the most widely used and efficient method for flood routing in hydrologic engineering; however, the applications of this model still suffer from a lack of an efficient method for parameter estimation. Thus, in this paper, we present a hybrid particle swarm optimization (HPSO) to estimate the Muskingum model parameters by employing PSO hybridized with Nelder---Mead simplex method. The HPSO algorithm does not require initial values for each parameter, which helps to avoid the subjective estimation usually found in traditional estimation methods and to decrease the computation for global optimum search of the parameter values. We have carried out a set of simulation experiments to test the proposed model when applied to a Muskingum model, and we compared the results with eight superior methods. The results show that our scheme can improve the search accuracy and the convergence speed of Muskingum model for flood routing; that is, it has higher precision and faster convergence compared with other techniques.

Hybrid particle swarm optimization for parameter estimation of Muskingum model

In order to accelerate the convergence and improve the calculation accuracy for parameter optimization of the Muskingum model, we propose a novel, adaptive hybrid particle swarm optimization (AHPSO) algorithm. With the decreasing of inertial weight factor proposed, this method can gradually converge to a global optimal with elite individuals obtained by hybrid PSO. In the paper, we analyzed the feasibility and the advantages of the AHPSO algorithm. Then, we verified its efficiency and superiority by application of the Muskingum model. We intensively evaluated the error fitting degree based on the comparison with four known formulas: the test method (TM), the least residual square method (LRSM), the nonlinear programming method (NPM), and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. The results show that the AHPSO has a higher precision. In addition, we compared the AHPSO algorithm with the binary-encoded genetic algorithm (BGA), the Gray genetic algorithm (GGA), the Gray-encoded accelerating genetic algorithm (GAGA) and the particle swarm optimization (PSO), and results show that AHPSO has faster convergent speed. Moreover, AHPSO has a competitive advantage compared with the above eight methods in terms of robustness. With the efficiency of this approach it can be extended to estimate parameters of other dynamic models.

Estimating parameters of muskingum model using an adaptive hybrid pso algorithm

The widespread adoption and evolution of Software Defined Networking (SDN) have enabled the service providers to successfully simplify network management. Along with the traffic explosion, there is decreasing CAPEX and OPEX as well as an increase in the average revenue per user. However, this wide adoption of SDNs is posing real challenges and concerns in terms of security aspects. The main challenges are how to provide proper authentication, access control, data privacy, and data integrity among others for the API-driven orchestration of network routing. Herein, the Software Defined Perimeter (SDP) is proposed as a framework to provide an orchestration of connections. The expectation is a framework that restricts network access and connections between objects on the SDN-enabled network infrastructures. There are several potential benefits as a result of the integration between SDP systems and SDNs. In particular, it provides a completely scalable and managed security solution. Consequently, it leads to flexible deployment that can be tailored to fit the need of any generic network security perimeter. The proposed Integrated frameworks are examined through virtualized network testbeds. The testing results demonstrate that the proposed framework is malleable to both port scanning (PS) attack and Denial of Service (DoS) bandwidth attack. In addition, it clarifies some interesting potential integration points between the SDP systems and SDNs to further research in this area.

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On the Security of SDN: A Completed Secure and Scalable Framework Using the Software-Defined Perimeter

In a Cloud computing environment, the workload fluctuates dynamically producing undesirable situations such as load imbalance, lower utilization or workload hotspots. In such cases, virtual machine migration is a potential solution. However, an algorithm based on a single objective (e.g. service-level agreement) is usually used to direct the migration process. On the contrary, there exist unconsidered conflicting factors impacting the migration process such as load volume, power consumption and resource wastage. In this paper, we consider the migration process as a multiobjective problem where the objectives are typically non-commensurable. Therefore, we propose a novel migration policy consolidated by a new elastic multi-objective optimization strategy to evaluate different objectives (including migration cost) simultaneously, and to provide the flexibility for manipulating different cases. We have tested the proposed policy through an extensive set of simulation experiments using CloudSim, and the results ensure the efficiency of our policy to control the system performance by adjusting the migration objectives to suit various workload situations.

A Multi-objective Virtual Machine Migration Policy in Cloud Systems

The high efficiency video coding (HEVC) partial encryption (PE) technique depends on encrypting the highly sensitive data on the video bit stream. The HEVC PE technique should keep the video format compliance, should be of the same bit rate, and ensure real-time constraints. The paper suggests an effective RC6 HEVC PE technique which encrypts sensible video data bits with low complexity overhead, fast encoding time for real-time applications, and fixed HEVC bitrate. These features result from using the low computational complexity RC6 block cipher for encrypting the selective video bins. The proposed RC6 HEVC PE encrypts the discrete cosine transform (DCT) coefficients sign bit, the DCT remaining absolute values suffixes that are binarized by Exp-Golomb (EGk) order zero, the motion vector difference (MVD) sign bits, and MVD absolute values suffixes that are binarized by EGk order one. Also, this paper introduces experimental results that compare between the proposed RC6 HEVC PE and HEVC PE algorithms that use the Advanced Encryption Standard in different operation modes. This paper presents more details about the security analysis of RC6 HEVC PE including encryption quality test, key space test, statistical analysis such as histogram and correlation coefficient analysis, and sensitivity analysis such as the key sensitivity analysis. The achieved test results ensured and confirmed the security, reliability, and robustness of RC6 HEVC PE technique.

Ahmed Sallam

Papers

Hybrid particle swarm optimization for parameter estimation of Muskingum model

Estimating parameters of muskingum model using an adaptive hybrid pso algorithm

On the Security of SDN: A Completed Secure and Scalable Framework Using the Software-Defined Perimeter

A Multi-objective Virtual Machine Migration Policy in Cloud Systems

HEVC Selective Encryption Using RC6 Block Cipher Technique