Showing papers by "Balamurugan Balusamy published in 2017"

Computationally efficient privacy preserving authentication and key distribution techniques for vehicular ad hoc networks

Abstract: The incorporation of electronics by embedding the relevant sensors in the physical devices in home and office, vehicles of all types, buildings in the smart cities and in all possible spheres of life form a network of devices termed as internet of things (IoT). It is being realized that vehicular ad-hoc networks (VANETs) which are responsible for the reliable and secure communication among vehicles is a primary area of research in IoT and hence ensuring security in this area is essential. Thus, this work introduces a novel approach to improve the existing authentication support to VANETs. In this proposed framework, first an anonymous authentication approach for preserving the privacy is proposed which not only performs the vehicle user’s anonymous authentication but preserves the message integrity of the transmitting messages as well. Although many anonymous authentication schemes have been proposed in VANETs until now, the existing schemes suffer from a high computation cost during the signature and certificate verification process which leads to delayed authentication. Consequently, the vehicles and roadside units (RSUs) cannot authenticate more number of vehicles per second in VANETs. Second, an efficient anonymous group key distribution protocol is proposed in this paper for securely distributing the group key to the group of vehicles in the communication range of an RSU. The RSUs can send location based information to the group of vehicles in a secure manner using this group key. Experimental analysis portrays that the results of this new privacy preserving anonymous authentication and key management schemes are promising and efficient with regard to signature verification cost and computational cost in comparison with the existing schemes.

Cloud Computing: Fundamentals and Research Issues

Abstract: Nowadays, access control and data security are two most critical problems with cloud computing. Access control can be defined as a procedure by which users can access data from the cloud server. At the time of accessing data, there are many problems, such as data security, high data accessing time, data lost, overhead, data redundancy, etc. In the first part of this paper, a brief discussion of fundamentals of cloud computing are presented. Moreover, all the issues of cloud computing are also discussed in this paper. Finally, future work directions have been identified for the cloud computing environment.

Data accessing based on the popularity value for cloud computing

Abstract: Nowadays, Big Data (BD) is one of the advanced areas of Information Technology (IT) sector BD can be explained as a large volume of file or data Since there are many hackers and malicious users over the internet, it is very important to distinguish between the authorized and unauthorized users for securing the confidentiality of BD Access control method allows data accessing of an authorized user In recent years, file or data access control is a very challenging issue in BD Existing access control schemes mainly focus on the confidentiality of the data storage from the unauthorized users In this paper, a novel file or data access control scheme has been presented The proposed scheme allows reducing the searching cost and accessing time, while providing BD to the user It also minimizes the problem of data redundancy

A Secured Access Control Technique for Cloud Computing Environment Using Attribute Based Hierarchical Structure and Token Granting System

Abstract: Cloud computing has drastically condensed the computational and storage costs of outsourced data. The existing access control techniques offer users access provisions centered on the common user attributes like Roles, which reduces the fine-grained access measure. The paper defines a Storage Correctness and Fine-grained Access Provision (SCFAP) scheme, that provides the user an exclusive access through the use of a hierarchical structure which is a combination of users unique and common attributes. Also, we deploy the concept of Token Granting system that allows the users to verify the correctness of outsourced data without the retrieval of the respective files. The tokens are derived from the metadata containing file location that helps in the process of storage correctness verification and improvises the storage efficiency. The experimental results show SCFAP has improved storage efficiency and error recovery measures than existing techniques.

Energy-Aware Fruitfly Optimisation Algorithm for Load balancing in Cloud Computing Environments

Abstract: An effective task scheduling is one of the vital aspects for effectually hitching the potential of cloud computing. The most important aspect of task scheduling focuses on balancing the load of tasks among virtual machines, which is independent in nature. Energy conservation is one of the major key issues in cloud environment which in turn reduces operation costs in cloud datacenter. Meanwhile, Energy-aware load balancing optimisation technique is a promising way to attain the goal. To ensure fast processing time and optimum utilization of the cloud resources, we propose an energy-aware Fruit fly optimisation algorithm (EFOA-LB) for balancing the load among virtual machines in the cloud system. The energy-aware EFOA-LB is a modern swarm intelligence algorithm inspired by the foraging behavior of fruit flies, aims to attain well-balanced load on virtual machines and reduces energy consumption accordingly. Based on results obtained from our simulations, the proposed algorithms minimizes makespan and reduces the energy consumption of the datacenter, while meeting the task performance. The experiment results indicate that the energy-aware EFOA-LB algorithm is more efficient than the existing load balancing algorithms.

Availability modeling for multi-tier cloud environment

Abstract: Performance modeling forms an essential process for evaluation of cloud quality. Cloud performance appraisal process and methods widely differ from that of other proven performance related methodologies being used for domains like computer networks, distributed computing and operations systems. Multi-tier Cloud is a scalable system in which many services or tiers can be constructed for all types of applications. The quality of service of a Multi-tier cloud environment is closely associated with several factors like dependability, availability, reliability, security, perform-ability and each of the performance entities directly or indirectly influences the overall functioning of the cloud. There are many models to evaluate cloud performance and quality, but these traditional models are not efficient enough and consider only certain primary parameters in their evaluation. In this paper, a high-level performance analysis model is proposed that can predict the availability of a Multi-tier cloud enviro...

Predictive Analysis for Digital Marketing Using Big Data: Big Data for Predictive Analysis

Abstract: Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities. Predictive Analysis for Digital Marketing Using Big Data: Big Data for Predictive Analysis

Ant colony-based load balancing and fault recovery for cloud computing environment

Abstract: Scheduling a task and recovering resources in cloud computing is an important optimisation problem. Workload balancing of preemptive and non-preemptive task on the VM is a significant phase in task scheduling. The load of overloaded VM and under-loaded VM has to be balanced to achieve optimal machine utilisation. The recovery process is an essential phase when the failure in VM occurs. The resources that have been stored in the failed VM have to be recovered. The recovery process is done to reclaim the task that have been failed in the VM. In this paper, an algorithm named ant colony-based load balancing and fault recovery (ACB-LBR) is proposed that achieve well-balanced load across VM, activates recovery process at the time of VM failure and reduces power consumption among VMs. This algorithm uses for aging behaviour of Ant colony for balancing the tasks in overloaded VM that leads to high throughput. The ACB-LBR algorithm recovers the lost resource at failure time and manages less power consumption. The experimental results show that the proposed algorithm is effective compared to existing load balancing, recovery and power consumption algorithm.

Analysis of Measures to Achieve Resilience during Virtual Machine Interruptions in IaaS Cloud Service

Abstract: In cloud computing era, the resilience issues faced by cloud computing services may be high. And therefore, the best alternative to reckon with the effects on the Quality-of-Service is to preserve resilience of Cloud computing service. To address this issue, an analytical model is proposed to study queueing system to handle various virtual machine interruptions. The proposed model recommends a secondary virtual machine to redeem the primary virtual machine during a probable halt. The work highlights the innovation employed for analysing the measures to achieve resilience during virtual machine interruptions in IaaS cloud service, the main objective of this research. The model is simulated using SHARPE and the results declare guaranteed performance for the IaaS clients to achieve high availability of service as the response time never deflate during VM interruptions.

Simplified and efficient framework for managing roles in cloud-based transaction processing systems using attribute-based encryption

Abstract: Cloud storage is utilised in a dynamic collaboration environment, which creates the need for improved access control techniques protecting outsourced data from security risk exposures. This includes the provision of different level of abstraction, which allows the administrators to implement high level access policies, independent of low level infrastructures constituting the cloud services. In this paper, we define a secured access provision with effective role management (SAPERM) scheme, which provides a clear description of the algorithmic steps associated with system framework establishment and user access provision for cloud-based enterprises. The implementation of SAPERM scheme provides a clear mapping between the system entities solving the problems related to user revocation, delegation and user access provisions. We further deduce an effective algorithm for session scheduling and data archival that results in a highly secure and consistent system framework. The experimental implementation of SAPERM over Nova open stack cloud provides standard access provision and better system performance even at higher process threshold rate. This scheme is validated using Nova open stack compute, a cloud computing environment.

Social Network Web Mining: Web Mining Techniques for Online Social Network Analysis

Abstract: Today, social networks are major part of everyone’s lives. They provide means to communicate with people across the globe with ease. As of July 2016, there are over 1.71 billion monthly active Facebook users. They generate significant amount of data, which if analysed well will provide us with valuable information. This can be done by analysing the log data collected at the respective social networking service. This chapter focuses on extraction and analysis of Facebook data since it is presently the most used social network. The result of analysis can be used in building decision support systems for an organization to help with the decision making process.

Achieving fine-grained access control and mitigating role explosion by utilising ABE with RBAC

Abstract: Cloud systems can store a vast amount of sensitive data whose access must be well regulated. A good access control policy ensures the security of this data while providing high flexibility in terms of access management. In this paper, we introduce access control architecture to mitigate the issue of role-explosion in RBAC and achieve a high degree of fine-grained access control by following an attribute-based encryption scheme with RBAC. In our model, we propose a user-tree with a hierarchical structure composed of groups and sub-groups to which a user will be assigned. These sub-groups will have their own sets of attributes as well as common inherited attributes. A user assigned to a specific sub-group will receive a key with the specific attributes of the sub-group as well as the inherited attributes.

Enhancing Quality of Service in Cloud Gaming System: An Active Implementation Framework for Enhancing Quality of Service in Multi-Player Cloud Gaming

Abstract: In multi-player cloud gaming two or more people from different locations may actively participate in gaming as like they were in a similar geographical location. In such cases handling massive user inputs, performance rendering, bandwidth fluctuations, load balancing, data capturing, data transmission in real time still remains a cumbersome in cloud gaming. In this chapter, we propose a framework that overcomes the major issues associated with quality of service in cloud gaming. The cloud platform consists of two environments namely workbench and runtime environment, where the work bench environment comprises of tools like end user Enhancing Quality of Service in Cloud Gaming System

Hybrid Approach for Data Classification in E-Health Cloud

Abstract: The growth of IT industry technology is absorbed by the cloud service technology, which leads to secure connectivity and availability of services to cloud users. This paper proposes an e-health data classification system that gives the services to e-health cloud user for their disease prediction requirements. The tree bagging method is suitable to select better weighted attributes and the careful seeding of K-means ++ algorithm improves the accuracy and speed of clustering. Most of the tree classification methods use only information gain as the strategy to select suitable attributes for classification. We used information gain in bagging technique to improve accuracy. In this research article, we proposed a method to Coalescing Decision tree classification algorithm, bagging technique, and K-means++ algorithm to build a better classifier for the e-health cloud users. It was evaluated with the standard data sets such as Diabetes, breast cancer, liver disorders and cardiotocography.