Predictive Analysis of Ransomware Attacks using Context-aware AI in IoT Systems

Ransomware attacks are emerging as a major source of malware intrusion in recent times. While so far ransomware has affected general-purpose adequately resourceful computing systems, there is a visible shift towards low-cost Internet of Things systems which tend to manage critical endpoints in industrial systems. Many ransomware prediction techniques are proposed but there is a need for more suitable ransomware prediction techniques for constrained heterogeneous IoT systems. Using attack context information profiles reduces the use of resources required by resource-constrained IoT systems. This paper presents a context-aware ransomware prediction technique that uses context ontology for extracting information features (connection requests, software updates, etc.) and Artificial Intelligence, Machine Learning algorithms for predicting ransomware. The proposed techniques focus and rely on early prediction and detection of ransomware penetration attempts to resource-constrained IoT systems. There is an increase of 60 % of reduction in time taken when using context-aware dataset over the non-context aware data.