Continuous certification of non-repudiation in cloud storage services

This paper presents a certification model for Non-repudiation (NR) of cloud storage services. NR, i.e., The possession of proofs that certain exchanges have taken place amongst interacting parties, is a significant security property for cloud data storage services. Our model for certifying NR is based on continuous monitoring and has been defined and realised according to the CUMULUS approach. It also corresponds to certification of level 3 maturity in the reference certification framework of Cloud Security Alliance

Monitoring-Based Certification of Cloud Service Security

In this paper, we present a novel approach to cloud service security certification. This approach could be used to: (a) define and execute automatically certification models, which can continuously and incrementally acquire and analyse evidence regarding the provision of services on cloud infrastructures through continuous monitoring; (b) use this evidence to assess whether the provision is compliant with required security properties; and (c) generate and manage digital certificates confirming the compliance of services if the acquired evidence supports this. We also present the results of an initial experimental evaluation of our approach based on the MySQL server and RUBiS benchmark

Incremental certification of cloud services

Cloud is becoming fast a critical infrastructure. However, several recent incidents regarding the security of cloud services clearly demonstrate that security rightly remains one of the major concerns of enterprises and the general public regarding the use of the cloud. Despite advancements of research related to cloud security, we are still not in a position to provide a systematic assessment of cloud security based on real operational evidence. As a step towards addressing this problem, in this paper, we propose a novel approach for certifying the security of cloud services. Our approach is based on the incremental certification of security properties for different types of cloud services, including IaaS, PaaS and SaaS services, based on operational evidence from the provision of such services gathered through continuous monitoring. An initial implementation of this approach is presented

Cloud Certification Process Validation using Formal Methods

The importance of cloud-based systems is increasing constantly as they become crucial for completing tasks in an effective and affordable manner. Yet, their use is affected by concerns about the security of the data and applications provisioned through them. Security certification provides a means of increasing confidence in such systems, by establishing that they fulfil certain security properties of interest. Certification processes involve security property assessments against specific threat models. These processes may be based on self-assessment, testing, inspection or runtime monitoring of security properties, and/or combinations of such methods (hybrid certification). One important question for all such processes is whether they actually deliver what they promise. This question is open at the moment and is the focus of our work. To address it, we have developed an approach that formalises certification processes, by translating them in the language of the Prism model-checker and uses Prism to verify properties of interest on the model of the certification process, under specific environmental assumptions

Big Data Assurance Evaluation: An SLA-Based Approach.

The Big Data community has started noticing that there is the need to complete Big Data platforms with assurance techniques proving the correct behavior of Big Data analytics and management. In this paper, we propose a Big Data assurance solution based on Service-Level Agreements (SLAs), focusing on a platform providing Model-based Big Data Analytics-as-a-Service (MBDAaaS)

Model driven certification of Cloud service security based on continuous monitoring

Cloud Computing technology offers an advanced approach for the provision of infrastructure, platform and software services without the need of extensive cost of owning, operating or maintaining the computational infrastructures required. However, despite being cost effective, this technology has raised concerns regarding the security, privacy and compliance of data or services offered through cloud systems. This is mainly due to the lack of transparency of services to the consumers, or due to the fact that service providers are unwilling to take full responsibility for the security of services that they offer through cloud systems, and accept liability for security breaches [18]. In such circumstances, there is a trust deficiency that needs to be addressed. The potential of certification as a means of addressing the lack of trust regarding the security of different types of services, including the cloud, has been widely recognised [149]. However, the recognition of this potential has not led to a wide adoption, as it was expected. The reason could be that certification has traditionally been carried out through standards and certification schemes (e.g., ISO27001 [149], ISO27002 [149] and Common Criteria [65]), which involve predominantly manual systems for security auditing, testing and inspection processes. Such processes tend to be lengthy and have a significant financial cost, which often prevents small technology vendors from adopting it [87]. In this thesis, we present an automated approach for cloud service certification, where the evidence is gathered through continuous monitoring. This approach can be used to: (a) define and execute automatically certification models, to continuously acquire and analyse evidence regarding the provision of services on cloud infrastructures through continuous monitoring; (b) use this evidence to assess whether the provision is compliant with required security properties; and (c) generate and manage digital certificates to confirm the compliance of services with specific security properties

Validation of Service Level Agreements using Probabilistic Model Checking

With the fast growth of Information Technology (IT), organisations rely mostly on web services, cloud services and recently on Big Data Analytics services (BDA services), in order to support their business services. To securely use these services, service clients sign a Service Level Agreement (SLA) with service providers, regarding a particular service provision. Typically, SLAs define the properties that need to be preserved during the provision of a service (e.g., quality of service properties) and actions that will be applied if the service provision violates the defined properties (e.g., penalties or renegotiation). Whilst significant research has focused on monitoring SLAs during the provision of services, the exploration and validation of the potential consequences of SLAs for the involved parties prior to putting them in operation is not addressed by existing research. In this paper, we present an approach to SLA validation that is based model checking. Our approach is based on the translation of SLAs expressed in WSAgreement into models of the probabilistic model checker PRISM and the validation of SLA properties using the model checking capabilities of this tool

A Multi-Layer and Multi-Tenant Cloud Assurance Evaluation Methodology

Data with high security requirements is being processed and stored with increasing frequency in the Cloud. To guarantee that the data is being dealt in a secure manner we investigate the applicability of Assurance methodologies. In a typical Cloud environment the setup of multiple layers and different stakeholders determines security properties of individual components that are used to compose Cloud applications. We present a methodology adapted from Common Criteria for aggregating information reflecting the security properties of individual constituent components of Cloud applications. This aggregated information is used to categorise overall application security in terms of Assurance Levels and to provide a continuous assurance level evaluation. It gives the service owner an overview of the security of his service, without requiring detailed manual analyses of log files

Pattern Based Design and Verification of Secure Service Compositions

Ensuring the preservation of security is a key requirement and challenge for Service-Based Systems (SBS) due to the use of third party software services not operating under different security perimeters. In this paper, we present an approach for verifying the security properties of SBS workflows and adapting them if such properties are not preserved. Our approach uses secure service composition patterns. These patterns encode proven dependencies between service level and workflow level security properties. These dependencies are used in reasoning processes supporting the verification of SBS workflows with respect to workflow security properties and their adaptation in ways that guarantee the properties if necessary. Our approach has been implemented by extending the Eclipse BPEL Designer and validated experimentally. The experimental evaluation has produced positive results, indicating that even for complex workflows and large sets of secure service composition patterns verification can be performed efficiently

Big Data Assurance Evaluation: An SLA-Based Approach.

The Big Data community has started noticing that there is the need to complete Big Data platforms with assurance techniques proving the correct behavior of Big Data analytics and management. In this paper, we propose a Big Data assurance solution based on Service-Level Agreements (SLAs), focusing on a platform providing Model-based Big Data Analytics-as-a-Service (MBDAaaS)