Visible to the public A Framework for Insider Collusion Threat Prediction and Mitigation in Relational Databases

TitleA Framework for Insider Collusion Threat Prediction and Mitigation in Relational Databases
Publication TypeConference Paper
Year of Publication2019
AuthorsYaseen, Q., Alabdulrazzaq, A., Albalas, F.
Conference Name2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)
ISBN Number978-1-7281-0554-3
Keywordsauthorisation, cloud computing, Collaboration, collusion insider attacks, Collusion Threat, composability, Computer hacking, Data dependencies, database architect, database management systems, database schema, discovered collusion insider threat, Human Behavior, Information security, insider collusion threat prediction, insider threat, insiders accesses, Knowledgebase, Metrics, mitigating technique, Predictive models, pubcrawl, real time monitoring technique, Relational Database, relational database security, relational database systems, relational databases, remuneration, resilience, Resiliency, robust technique

This paper proposes a framework for predicting and mitigating insider collusion threat in relational database systems. The proposed model provides a robust technique for database architect and administrators to predict insider collusion threat when designing database schema or when granting privileges. Moreover, it proposes a real time monitoring technique that monitors the growing knowledgebases of insiders while executing transactions and the possible collusion insider attacks that may be launched based on insiders accesses and inferences. Furthermore, the paper proposes a mitigating technique based on the segregation of duties principle and the discovered collusion insider threat to mitigate the problem. The proposed model was tested to show its usefulness and applicability.

Citation Keyyaseen_framework_2019