Combating Data Leakage in the Cloud

Abstract

The increasing number of reports on data leakage incidents increasingly erodes the already low consumer confidence in cloud services. Hence, some organisations are still hesitant to fully trust the cloud with their confidential data. Therefore, this study raises a critical and challenging research question: How can we restore the damaged consumer confidence and improve the uptake and security of cloud services? This study makes a plausible attempt at unpacking and answering the research question in order to holistically address the data leakage problem from three fronts, i.e. conflict-aware virtual machine (VM) placement, strong authentication and digital forensic readiness. Consequently, this study investigates, designs and develops an innovative conceptual architecture that integrates conflict-aware VM placement, cutting-edge authentication and digital forensic readiness to strengthen cloud security and address the data leakage problem in the hope of eventually restoring consumer confidence in cloud services. The study proposes and presents a conflict-aware VM placement model. This model uses varying degrees of conflict tolerance levels, the construct of sphere of conflict and sphere of non-conflict. These are used to provide the physical separation of VMs belonging to conflicting tenants that share the same cloud infrastructure. The model assists the cloud service provider to make informed VM placement decisions that factor in their tenants’ security profile and balance it against the relevant cost constraints and risk appetite. The study also proposes and presents a strong risk-based multi-factor authentication mechanism that scales up and down, based on threat levels or risks posed on the system. This ensures that users are authenticated using the right combination of access credentials according to the risk they pose. This also ensures end-to-end security of authentication data, both at rest and in transit, using an innovative cryptography system and steganography. Furthermore, the study proposes and presents a three-tier digital forensic process model that proactively collects and preserves digital evidence in anticipation of a legal lawsuit or policy breach investigation. This model aims to reduce the time it takes to conduct an investigation in the cloud. Moreover, the three-tier digital forensic readiness process model collects all user activity in a forensically sound manner and notifies investigators of potential security incidents before they occur. The current study also evaluates the effectiveness and efficiency of the proposed solution in addressing the data leakage problem. The results of the conflict-aware VM placement model are derived from simulated and real cloud environments. In both cases, the results show that the conflict-aware VM placement model is well suited to provide the necessary physical isolation of VM instances that belong to conflicting tenants in order to prevent data leakage threats. However, this comes with a performance cost in the sense that higher conflict tolerance levels on bigger VMs take more time to be placed, compared to smaller VM instances with low conflict tolerance levels. From the risk-based multifactor authentication point of view, the results reflect that the proposed solution is effective and to a certain extent also efficient in preventing unauthorised users, armed with legitimate credentials, from gaining access to systems that they are not authorised to access. The results also demonstrate the uniqueness of the approach in that even minor deviations from the norm are correctly classified as anomalies. Lastly, the results reflect that the proposed 3-tier digital forensic readiness process model is effective in the collection and storage of potential digital evidence. This is done in a forensically sound manner and stands to significantly improve the turnaround time of a digital forensic investigation process. Although the classification of incidents may not be perfect, this can be improved with time and is considered part of the future work suggested by the researcher.