Threat Management in Data-centric IoT-Based Collaborative Systems

Abstract

In this article, we propose a threat management system (TMS) for Data-centric Internet-of-Things-based Collaborative Systems (DIoTCSs). In particular, we focus on tampering attacks that target shared databases and can affect the execution of the DIoTCS services. The novelty of the proposed system is to isolate the damage caused by tampering attacks into data partitions. We formulate the partitioning problem as a cost-driven optimization problem, prove its NP-hardness, and propose two polynomial-time heuristics. We evaluate a TMS experimentally and demonstrate that intelligent partitioning of the database improves the overall availability of the DIoTCS.