Mitigating insider threat in cloud relational databases

Abstract

Cloud security has become one of the emergent issues because of the immense growth of cloud services. A major concern in cloud security is the insider threat because of the harm that it poses. Therefore, defending cloud systems against insider attacks has become a key demand. This work deals with insider threat in cloud relational database systems. It reveals the flaws in cloud computing that insiders may use to launch attacks and discusses how load balancing across availability zones may increase insider threat. To mitigate this kind of threat, the paper proposes four models, which are peer‐to‐peer model, centralized model, Mobile‐Knowledgebases model, and Guided Mobile‐Knowledgebases model, and it discusses their advantages as well as their limitations. Moreover, the paper provides experiments and analysis that compare among the proposed models, demonstrate their effectiveness, and show the conditions under which they work with highest performance. Copyright © 2016 John Wiley & Sons, Ltd.

Self-adaptive authorisation in OpenStack cloud platform

“…Table 6 presents the results obtained when considering scenario #5, where N users exploit 1 role for abusing 1 service. In this case, we have kept fixed the number of common users to 100, whilst varying the number of abusing users, according to existing similar experiments [25]. We noticed a detection time between 6 and 7 seconds for different numbers of malicious users.…”

“…For the performance experiments in our private Open-Stack cloud, we have considered a population of up to 1000 users, which is a reasonable number considering other similar works. For instance, Feng et al [15] used Symantec Box cloud file-sharing access log data to detect insider threats in a population of 688 users, and Yaseen et al [25] used up to 500 users to assess the performance of models to detect insider threats.…”

Correlation‐based sequence alignment models for detecting masquerades in cloud computing

“…Furthermore, this algorithm restricts ordering between items and rates similar rules differently. Yaseen et al [21] discuss how load balancing across availability zones may increase insider attacks. They deal with insider attacks in cloud relational database systems by‫ا‬revealing the flaws‫ا‬in cloud‫ا‬computing‫ا‬that insiders may‫ا‬use to launch attacks.…”

Hierarchical detection of insider attacks in cloud computing systems

“…The knowledgebase technique was extended to cloud computing systems. It aims to achieve advance detection of possible insider threats (Yaseen et al, 2013(Yaseen et al, , 2016Althebyan et al, 2015).…”