A Contextual Derivation Algorithm for Cybersecurity in IoT Environments

The increase in the number of IoT devices invading both private and professional spaces opens the way for different cyberattacks. Facing such threats requires new approaches capable of reading the context of a given situation and acting accordingly to protect the users’ data and applications. Logical-based approaches can be harnessed in this case because of the semantic dimension they model and implement. In this paper, we make use of an existential rule-based knowledge base to model an IoT environment and detect anomalies as inconsistencies inside the obtained logical system. Accordingly, we first introduce an algorithm for knowledge base rewriting into a context-based knowledge base. Then, we detail our contextualized derivation algorithm for inconsistency detection in such a logical system.