The Cloud Computing concept offers dynamically scalable resources provisioned as a service over the Internet.Economic benefits are the main driver for the Cloud, since it promises the reduction of capital expenditure and operational expenditure.In order for this to become reality, however, there are still some challenges to be solved. Amongst these are security and trust issues, since the user data has to be released to the Cloud and thus leaves the protection sphere of the data owner. Most of the discussions on these topics are mainly driven by arguments related to organisational means. This paper focuses on various security issues arising from the usage of Cloud services and especially by the rapid development of Cloud computing arena. It also discusses basic security model followed by various High Level Security threats in the industry.

Security Issues on Cloud Computing.

This paper presents a comprehensive study on the challenges and issues of security in cloud computing. We first look into the impacts of the distinctive characteristics of cloud computing, namely, multi-tenancy, elasticity and third party control, upon the security requirements. Then, we analyze the cloud security requirements in terms of the fundamental issues, i.e., confidentiality, integrity, availability, trust, and audit and compliance. Furthermore, we discuss the taxonomy for security issues in cloud computing. Finally, we summarize the security issues in cloud computing by a cloud security architecture.

Security issues in cloud computing

The Intelligent computing system, described to be a collection of the connected device working in mutual understanding to attain a particular purpose, is an incorporation of artificial intelligence and the computational intelligence, and are employed in variety of applications. The paper presents the survey on the data mining algorithms and the techniques that could be employed with the intelligent computing system, presenting a basic conception of the data mining along with the prominent algorithms of the data mining and the classification of its techniques, further the survey concludes with the challenges included in the overview of the survey done along with the future enhancement in the research that analyses the data mining techniques in the intelligent computing applications.

https://www.irojournals.com/tcsst/V1/I1/02.pdf

Survey of data mining algorithm’s for intelligent computing system

The Internet of Things (IoT) is a network of different objects that refers to an environment in which intelligent devices around us can connect to the Internet and exchange information together. A large number of generated events from IoT objects causes overhead on the network. Therefore, to optimize the usage of IoT network, it is essential to provide solutions for network problems including scalability, routing, reliability, security, energy conservation, network lifetime, congestion, heterogeneity, and quality of service (QoS). In this regard, load balancing as an efficient method takes a significant role in distributing loads among different routes. Imbalance traffic load across the network causes high latency in some routes and loss of data packets and decreases packet delivery ratio. Although load balancing has a critical importance in the IoT, there is still a lack of an organized and comprehensive review about analyzing and examining its remarkable methods. Therefore, this paper by adopting a systematic manner aims to address this gap. In this research, the load balancing methods are categorized into two main categories including centralized and distributed and their merits and demerits are specified. Moreover, vital parameters, the challenges, and open issues in this scope are also discussed. Thus, future authors will be able to develop more effective load balancing mechanisms.

A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things

Cloud Storage is an important part of Cloud Computing, and it provides a way to achieve large scale storage architecture. And virtual storage is a strategy for Cloud Storage. A load balancing virtual storage strategy (LBVS) is proposed in this paper. The contribution of this strategy is that, it provides a large scale net data storage model and Storage as a Service model based on Cloud Storage. Three layers architecture is used to achieve storage virtualization, and two load balancing modules are used to balance systems load. This paper describes details of LBVS strategy, the model of virtual storage as well as how to implement the LBVS with iRODS and two load balancing algorithms.

LBVS: A Load Balancing Strategy for Virtual Storage.

The security of cloud computing has always been an important aspect of quality of service from cloud service providers. However, cloud computing poses many new security challenges which have not been well investigated. This paper focusing on issues relating to the cloud data storage methods and security in virtual environment. We propose a method for providing data storage and security in cloud using public key cryptosystem RSA. Further, describes the security services includes key generation, encryption and decryption in virtual environment.

Data security in cloud using RSA

Cloud Computing is an emerging area in the field of information technology (IT). Load balancing is one of the main challenges in cloud computing. It is a technique which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overloaded. Load balancing techniques help in optimal utilization of resources and hence in enhancing the performance of the system. The goal of load balancing is to minimize the resource consumption which will further reduce energy consumption and carbon emission rate that is the dire need of cloud computing. This determines the need of new metrics, energy consumption and carbon emission for energy-efficient load balancing in cloud computing. This paper mainly focused on the concept of load balancing technique in cloud computing, the existing load balancing techniques and also discusses the different qualitative metrics or parameters like performance, scalability, associated overhead etc.

Load balancing techniques: Major challenge in Cloud Computing - a systematic review

Group-based emotion recognition (GER) is an interesting topic in both security and social area. In this paper, a GER with hybrid optimization based recurrent fuzzy neural network is proposed which is from video sequence. In our work, by utilizing the Neural Network the emotion recognition (ER) is performed from group of people. Initially, original video frames are taken as input and pre-process it from multi user video data. From this pre-processed image, the feature extraction is done by Multivariate Local Texture Pattern (MLTP), gray-level co-occurrence matrix (GLCM), and Local Energy based Shape Histogram (LESH). After extracting the features, certain features are selected using Modified Sea-lion optimization algorithm process. Finally, recurrent fuzzy neural network (RFNN) classifier based Social Ski-Driver (SSD) optimization algorithm is proposed for classification process, SSD is used for updating the weights in the RFNN. Python platform is utilized to implement this work and the performance of accuracy, sensitivity, specificity, recall and precision is evaluated with some existing techniques. The proposed method accuracy is 99.16%, recall is 99.33%, precision is 99%, sensitivity is 99.93% and specificity is 99% when compared with other deep learning techniques our proposed method attains good result.

/pdf/group-based-emotion-recognition-from-video-sequence-with-romh50zbw7.pdf

Group based emotion recognition from video sequence with hybrid optimization based recurrent fuzzy neural network

Cloud computing is one of today's most exciting technologies due to its ability to reduce costs associated with computing while increasing flexibility and scalability for computer processes. Security is a foremost important issue for data in cloud. To look after the data from cloud data storage we store the encrypted data in cloud environment. For encrypting the data we use different types of encryption methods like RSA, SHA1, and MD5. Further we also measure the performance of the different encrypted techniques based on the Key size of each encrypted technique to upload data in to the cloud by providing the best way security to data.

Performance evaluation of encryption techniques and uploading of encrypted data in cloud

Cloud computing is a paradigm where tasks are assigned to a combination of connections, software and services accessed over a network. Clouds provide processing power, which is made possible through distributed computing .In the Cloud Technology most organizations have own challenges when it comes to handle both critical and confidential information. The major challenge in cloud technology is how effectively the information is uploaded to the virtual data centers to ensure the privacy. To enhance the privacy the available existing cryptography techniques are symmetric and asymmetric in nature. We are focusing on enhancing the privacy when the information is uploaded with virtual data centers. In this context, our study is mainly focusing on encrypting the information before uploading with virtual data centers; we propose to implement using DES(Data Encryption Standards), as it is proved the best encryption performance results in terms of computing time.

Velagapudi Sreenivas

Papers

Data security in cloud using RSA

Load balancing techniques: Major challenge in Cloud Computing - a systematic review

Group based emotion recognition from video sequence with hybrid optimization based recurrent fuzzy neural network

Performance evaluation of encryption techniques and uploading of encrypted data in cloud

Enhancing the Security for Information with Virtual Data Centers in Cloud