Reputation based Trust Management System for Improving Public Health Care System in Pakistan

Health is the most important facility for the people by the government if it is provided properly.There is a lack of trust among people about healthcare system particularly in public hospitals ofPakistan. People do not trust doctors and hospitals due to poor management and low standardscheck-up by the doctors. This is due to the lack of feedback system which leads to themismanagement of healthcare institutions. In this research, we have developed a reputation-basedfeedback management system that will overcome all the problems related to the trust of the patientsregarding their medical care. This will enhance the system and will let doctors, staff, andmanagement to work honestly to make their repute well

Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR

Foundations of efficient virtual appliance based service deployments

The use of virtual appliances could provide a flexible solution to services deployment. However, these solutions suffer from several disadvantages: (i) the slow deployment time of services in virtual machines, and (ii) virtual appliances crafted by developers tend to be inefficient for deployment purposes. Researchers target problem (i) by advancing virtualization technologies or by introducing virtual appliance caches on the virtual machine monitor hosts. Others aim at problem (ii) by providing solutions for virtual appliance construction, however these solutions require deep knowledge about the service dependencies and its deployment process. This dissertation aids problem (i) with a virtual appliance distribution technique that first identifies appliance parts and their internal dependencies. Then based on service demand it efficiently distributes the identified parts to virtual appliance repositories. Problem (ii) is targeted with the Automated Virtual appliance creation Service (AVS) that can extract and publish an already deployed service by the developer. This recently acquired virtual appliance is optimized for service deployment time with the proposed virtual appliance optimization facility that utilizes active fault injection to remove the non-functional parts of the appliance. Finally, the investigation of appliance distribution and optimization techniques resulted the definition of the minimal manageable virtual appliance that is capable of updating and configuring its executor virtual machine. The deployment time reduction capabilities of the proposed techniques were measured with several services provided in virtual appliances on three cloud infrastructures. The appliance creation capabilities of the AVS are compared to the already available virtual appliances offered by the various online appliance repositories. The results reveal that the introduced techniques significantly decrease the deployment time of virtual appliance based deployment systems. As a result these techniques alleviated one of the major obstacles before virtual appliance based deployment systems

Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we  evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%

Libro de Actas JCC&BD 2018 : VI Jornadas de Cloud Computing & Big Data

Se recopilan las ponencias presentadas en las VI Jornadas de Cloud Computing & Big Data (JCC&BD), realizadas entre el 25 al 29 de junio de 2018 en la Facultad de Informática de la Universidad Nacional de La Plata.Universidad Nacional de La Plata (UNLP) - Facultad de Informátic

Systematic survey on evolution of cloud architectures

Cloud architectures are becoming an active area of research. The quality and durability of a software system are defined by its architecture. The architecture approaches that are used to build cloud-based systems are not available in a blended fashion to achieve an effective universal architecture solution. The paper aims to contribute to the systematic literature review (SLR) to assist researchers who are striving to contribute in this area. The main objective of this review is to systematically identify and analyse the recently published research topics related to software architecture for cloud with regard to research activity, used tools and techniques, proposed approaches, domains. The applied method is SLR based on four selected electronic databases proposed by (Kitchenham and Charters, 2007). Out of 400 classified publications, we regard 121 as relevant for our research domain. We outline taxonomy of their topics and domains, provide lists of used methods and proposed approaches. At present, there is little research coverage on software architectures for cloud, while other disciplines have become more active. The future work is to develop a secure architecture to achieve quality of service and service level agreements

A fuzzy load balancer for adaptive fault tolerance management in cloud platforms

This work was partly supported by IC4 (the Irish Centre for Cloud Computing and Commerce), funded by EI and the ID