Biblio

Cloud Computing is the most suitable environment for the collaboration of multiple organizations via its multi-tenancy architecture. However, due to the distributed management of policies within these collaborations, they may contain several anomalies, such as conflicts and redundancies, which may lead to both safety and availability problems. On the other hand, current cloud computing solutions do not offer verification tools to manage access control policies. In this paper, we propose a cloud policy verification service (CPVS), that facilitates to users the management of there own security policies within Openstack cloud environment. Specifically, the proposed cloud service offers a policy verification approach to dynamically choose the adequate policy using Aspect-Oriented Finite State Machines (AO-FSM), where pointcuts and advices are used to adopt Domain-Specific Language (DSL) state machine artifacts. The pointcuts define states' patterns representing anomalies (e.g., conflicts) that may occur in a security policy, while the advices define the actions applied at the selected pointcuts to remove the anomalies. In order to demonstrate the efficiency of our approach, we provide time and space complexities. The approach was implemented as middleware service within Openstack cloud environment. The implementation results show that the middleware can detect and resolve different policy anomalies in an efficient manner.

In big data environments with big number of users and high volume of data, we need to manage the corresponding huge number of security policies. Due to the distributed management of these policies, they may contain several anomalies, such as conflicts and redundancies, which may lead to both safety and availability problems. The distributed systems guided by such security policies produce a huge number of access logs. Due to potential security breaches, the access logs may show the presence of non-allowed accesses. This may also be a consequence of conflicting rules in the security policies. In this paper, we present an ongoing work on developing an environment for verifying and correcting security policies. To make the approach efficient, an access log is used as input to determine suspicious parts of the policy that should be considered. The approach is also made efficient by clustering the policy and the access log and considering separately the obtained clusters. The clustering technique and the use of access log significantly reduces the complexity of the suggested approach, making it scalable for large amounts of data.