CLE against SOA with Better Data Security Storage to Cloud 5G

Cloud 5G and Cloud 6G technologies are strong backbone infrastructures to provide high data rate and data storage with low latency for preserving QoS (Quality of Service) and QoE (Quality of Experience) in applications such as driverless vehicles, drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial-intelligence-based personalized assistants. There are many techniques to support the aforementioned applications, but for privacy preservation of Cloud 5G, the existing methods are still not sufficient. Public key encryption (PKE) scheme is an important means to protect user data privacy in Cloud 5G. Currently, the most common PKE used in Cloud 5G is CCA or CPA secure ones. However, its security level maybe not enough. SOA security is a stronger security standard than CPA and CCA. Roughly speaking, PKE with SOA security means that the adversary is allowed to open a subset of challenger ciphertexts and obtains the corresponding encrypted messages and randomness, but the unopended messages and randomness remain secure in the rest of the challenger ciphertexts. Security against SOA in PKEs has been a research hotspot, especially with the wide discussion in Cloud 5G. We revisited the SOA-CLE and proposed a new security proof, which is more concise and user friendly to understand privacy preservation in Cloud 5G applications.

CONSTRUCTION OF CUSTOMIZABLE SOA SECURITY FRAMEWORK USING ARTIFICIAL NEURAL NETWORKS

The Web Services technology for the implementation of Service Oriented Architecture (SOA) is the preferred choice in the current era of Enterprise Application Integration (EAI). As Web Services architecture is dynamic and loosely coupled, security aspects must be considered thoroughly at the time of designing. It is prone for attacks as it uses XML format for data exchange, which is a plain text. A novel security component named “Intelligent Security Engine (ISE)” is introduced into the proposed framework which incorporates Artificial Neural Networks (ANN) Learning Techniques for supervised knowledge acquisition on security threats of SOA. Thus, the proposed security framework is capable in the identification of future security vulnerabilities of SOA and can work effectively even for in-secured cross organizational EAI environment.

An Integrated Security Framework for SOA

In today's Web environment, Service Oriented Architecture (SOA) becomes an efficient paradigm to integrate distributed applications. Due to loosely coupled nature of SOA, security is one of the most important issues that must be considered in SOA-based environments. Most of the existing security solutions are proposed only from one certain point of view, and they are difficult to integrate together. In this paper, an integrated framework for SOA are proposed to provides an overall security solution, which contains a three-dimensional security model, a security architecture and related security standards.