Cloud computing adoption framework:A security framework for business clouds

This paper presents a Cloud Computing Adoption Framework (CCAF) security suitable for business clouds. CCAF multi-layered security is based on the development and integration of three major security technologies: firewall, identity management and encryption based on the development of Enterprise File Sync and Share technologies. This paper presents our motivation, related work and our views on security framework. Core technologies have been explained in details and experiments were designed to demonstrate the robustness of the CCAF multi-layered security. In penetration testing, CCAF multi-layered security could detect and block 99.95% viruses and trojans and could maintain 85% and above of blocking for 100 hours of continuous attacks. Detection and blocking took less than 0.012 second per trojan and viruses. A full CCAF multi-layered security protection could block all SQL injection providing real protection to data. CCAF multi-layered security had 100% rate of not reporting false alarm. All F-measures for CCAF test results were 99.75% and above. How CCAF multi-layered security can blend with policy, real services and blend with business activities have been illustrated. Research contributions have been justified and CCAF multi-layered security can offer added value for volume, velocity and veracity for Big Data services operated in the Cloud

Towards a Reuse Strategic Decision Pattern Framework – from Theories to Practices

© 2018 Springer Science+Business Media, LLC, part of Springer Nature This paper demonstrates our proposed Reuse Strategic Decision Pattern Framework (RSDPF) based on blending ANP and TOPSIS techniques, enabled by the OSM model with data analytics. The motivation, related work, theory, the use and deployment, and the service deployment of the framework have been discussed in details. In this paper, RSDPF framework is demonstrated by the data analysis and interpretations based on a financial service firm. The OSM model allows 3 step of processed to be performed in one go to perform statistical tests, identify linear relations, check consistency on dataset and calculate OLS regression. The aim is to identify the actual, expected and risk rates of profitability. Code and services can be reused to compute for analysis. Service integration of the RSDPF framework has been demonstrated. Results confirm that there is a high extent of reliability. In this paper, we have demonstrated the reuse and integration of the framework supported by the case study of the financial service firm with its data analysis and service to justify our research contributions – reuse and integration in statistical data mining, knowledge and heuristic discovery and finally domain transference

Introductory Editorial

The Open Journal of Big Data is a new open access journal published by RonPub, and RonPub is an academic publisher of online, open access, peer-reviewed journals. OJBD addresses aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents the two articles in this first issue. The first paper is on An Efficient Approach for Cost Optimization of the Movement of Big Data, which mainly focuses on the challenge of moving big data from one data center to other.The second paper is on Cognitive Spam Recognition Using Hadoop and Multicast-Update, which describes a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. OJBD has a rising reputation thanks to the support of research communities, which help us set up the First International Conference on Internet of Things and Big Data 2016 (IoTBD 2016), in Rome, Italy, between 23 and 25 April 2016

Disaster management system as an element of risk management for natural disaster systems using the PESTLE framework

Recently, we have witnessed so many natural catastrophes such as earthquakes in Japan, severe floods in the UK, US and many other parts of the world. Consequently businesses have been losing tens of billions of dollars as a result of various natural and man-made disasters. Disaster Management System (DMS) have proven to be important means for reducing risks associated with such damages to businesses. A DMS can minimize and in some cases, eliminates the risks through technical, management or operational solutions (risk management effort). However, it is virtually impossible to eliminate all risks. Information technology systems are vulnerable for a variety of disruptions (e.g. short-term power outage, disk drive failure) as a result of natural disasters to terrorist actions. In many cases, critical resources may reside outside the organizations control (such as telecommunications or electric power), and the organization may be unable to ensure their availability. This paper proposes a model for Disaster Management System as an Element of Risk Management using the PESTLE framework. Thus, an effective Disaster Management System in the form of contingency planning, execution and testing are essential to mitigate the risk of system and service availability. We have developed a global model for disaster recover planning and management based on the PESTLE framework which can be customised and applied to a variety of disasters prone systems such natural, emergency, IT/Network/Security, Data recovery, and incident-response systems. To summarise, this paper aims to maximise the benefits of PESTLE analysis it should be used on a regular basis within an organisation to enable the identification of trends