Biblio

Living in the age of digital transformation, companies and individuals are moving to public and private clouds to store and retrieve information, hence the need to store and retrieve data is exponentially increasing. Existing storage technologies such as DAS are facing a big challenge to deal with these huge amount of data. Hence, newer technologies should be adopted. Storage Area Network (SAN) is a distributed storage technology that aggregates data from several private nodes into a centralized secure place. Looking at SAN from a security perspective, clearly physical security over multiple geographical remote locations is not adequate to ensure a full security solution. A SAN security framework needs to be developed and designed. This work investigates how SAN protocols work (FC, ISCSI, FCOE). It also investigates about other storages technologies such as Network Attached Storage (NAS) and Direct Attached Storage (DAS) including different metrics such as: IOPS (input output per second), Throughput, Bandwidths, latency, cashing technologies. This research work is focusing on the security vulnerabilities in SAN listing different attacks in SAN protocols and compare it to other such as NAS and DAS. Another aspect of this work is to highlight performance factors in SAN in order to find a way to improve the performance focusing security solutions aimed to enhance the security level in SAN.

Secure computation is increasingly required, most notably when using public clouds. Many secure CPU architectures have been proposed, mostly focusing on single-threaded applications running on a single node. However, security for parallel and distributed computation is also needed, requiring the sharing of secret data among mutually trusting threads running in different compute nodes in an untrusted environment. We propose SDSM, a novel hardware approach for providing a security layer for directory-based distributed shared memory systems. Unlike previously proposed schemes that cannot maintain reasonable performance beyond 32 cores, our approach allows secure parallel applications to scale efficiently to thousands of cores.