There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

This paper presents a novel and robust chaos-based cryptosystem for secure transmitted images and four other versions. In the proposed block encryption/decryption algorithm, a 2D chaotic map is used to shuffle the image pixel positions. Then, substitution (confusion) and permutation (diffusion) operations on every block, with multiple rounds, are combined using two perturbed chaotic PWLCM maps. The perturbing orbit technique improves the statistical properties of encrypted images. The obtained error propagation in various standard cipher block modes demonstrates that the proposed cryptosystem is suitable to transmit cipher data over a corrupted digital channel. Finally, to quantify the security level of the proposed cryptosystem, many tests are performed and experimental results show that the suggested cryptosystem has a high security level.

A New Chaos-Based Cryptosystem for Secure Transmitted Images

Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.

Credit Risk Analysis Using a Reliability-Based Neural Network Ensemble Model

Definitely, cloud computing represents a real evolution in the IT world that provides many advantages for both providers and users. This new paradigm includes several services that allow data storage and processing. However, outsourcing data to the cloud raises many issues related to privacy concerns. In fact, for some organizations and individuals, data privacy present a crucial aspect of their business. Indeed, their sensitive data (health, finance, personal information, etc.) have a very important value, and any infringement of privacy can cause great loss in terms of money and reputation. Therefore, without considering privacy issues, the adoption of cloud computing can be discarded by large spectra of users. In this paper, we provide a survey on privacy risks and challenges for public cloud computing. We present and evaluate the main existing solutions that have made great progress in this area. To better address privacy concerns, we point out considerations and guidelines while giving the remained open issues that require additional investigation efforts to fulfill preserving and enhancing privacy in public cloud.

Privacy in cloud computing environments: a survey and research challenges

Background Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. Methods The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. Results The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). Conclusions The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.

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Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

Today, Internet is the primary medium for communication which is used by number of users across the Network. At the same time, its commercial nature is causing increase vulnerability to enhance cyber crimes and there has been an enormous increase in the number of DDOS (distributed denial of service attack) attacks on the internet over the past decade. Network resources such as network bandwidth, web servers and network switches are mostly the victims of DDoS attacks. In this paper basically summarizing different techniques of DDoS and its countermeasures by different methods such as Bloom Filter, Trace Back method, Independent Component Analysis and TCP Flow Analysis.

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A Review of DDOS Attack and its Countermeasures in TCP Based Networks

The back propagation algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a training algorithm consisting of a learning rate and a momentum factor. The major drawbacks of above learning algorithm are the problems of local minima and slow convergence speeds. The addition of an extra term, called a proportional factor reduces the convergence of the back propagation algorithm. We have applied the three term back propagation to multiplicative neural network learning. The algorithm is tested on XOR and parity problem and compared with the standard back propagation training algorithm.

Improved Back Propagation Algorithm to Avoid Local Minima in Multiplicative Neuron Model

In the present brutal competitive world, security is prime factor to transmit confidential data over the internet. Cryptographic algorithms play very important role in providing the data security against the various attacks. The specific characteristics of image, like high transmission rate with limited bandwidth, correlation among pixels, redundancy and requirement of high storage capacity makes traditional algorithms unsuitable for image encryption. To cross these boundaries for real time applications, design of new algorithms that require less computational power while preserving a sufficient level of security is always a big challenge for researchers. This paper proposes an algorithm based on block based image transformation using perfect shuffle operation followed by new encryption algorithm. In this paper we compare the generated results with available algorithms like AES, RC6 and BFS on the basis of two parameters entropy and correlation.

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A Novel Approach for Image Encryption by New M Box Encryption Algorithm using Block based Transformation along with Shuffle Operation

In mobile ad hoc networks, the quality of service (QoS) guarantees are more difficult when compared with wired networks due to mobility of nodes. The difficulties in the provision of such guarantees have limited the utility of MANETs. The AOMDV (Ad hoc On Demand Multipath Distance Vector) has a better routing protocol for efficient data delivery. In this paper we improve the QoS performance of AOMDV protocol and is called Enhanced AOMDV (EAOMDV) on the basis of queue length, which enhances the routing capability of AOMDV protocol. In this technique the queue length has handled the data and network performance has improved. The performance of both the protocol has been measured on the basis of performance metrics and packet loss.

QoS Enhancement of AOMDV Routing Protocol Using Queue Length Improvement

The modern technology defines cloud computing as a digital facility which is accessed by users over the web. The proposed framework provides the protection according to the classification of data. In this framework, cloud service provider (CSP) selects the eminent server using cuckoo algorithm with Markov chain process and Levy’s flight. After server selection, user encrypts their data using elliptic curve integrated encryption scheme (ECIES) at the user side and sends it to CSP for storage, where CSP stores the data after applying second encryption on it using advanced encryption standard (AES) at the cloud side. This double encryption provides the confidentiality on both the sides. This framework integrates server selection approach, authentication and encryption schemes in order to achieve the integrity, confidentiality as well as efficient computing.

Manish Manoria

Papers

A Review of DDOS Attack and its Countermeasures in TCP Based Networks

Improved Back Propagation Algorithm to Avoid Local Minima in Multiplicative Neuron Model

A Novel Approach for Image Encryption by New M Box Encryption Algorithm using Block based Transformation along with Shuffle Operation

QoS Enhancement of AOMDV Routing Protocol Using Queue Length Improvement

A Framework for Data Storage Security with Efficient Computing in Cloud