This research addresses the critical issue of cryptojacking attacks, a significant cybersecurity threat where malicious actors covertly exploit computational resources for unauthorized cryptocurrency mining, particularly in wireless sensor networks (WSN) and Internet of Things (IoT) devices. The article proposes an innovative approach that integrates time series analysis with graph neural networks (GNNs) to forecast/detect cryptojacking attack trends within these vulnerable ecosystems. Utilizing the “Cryptojacking Attack Timeseries Dataset,” the proposed method emphasizes early detection and predictive insights to anticipate emerging attack patterns. Through rigorous experiments, the model demonstrated high accuracy with ARIMA achieving up to 99.98% on specific attributes and the GNN model yielding an accuracy of 99.99%. Despite these strengths, the ensemble approach showed a slightly lower overall accuracy of 90.97%. Despite the reduction in accuracy compared to individual models, the ensemble method enhances predictive robustness and adaptability, making it more effective in identifying emerging cryptojacking trends amidst varying network conditions. This research significantly contributes to enhancing cybersecurity measures against the evolving threat of cryptojacking in WSN and IoT environments by providing a robust, proactive defence mechanism.

Purpose: Employing a fog computing (FC) network system in the robotic network system is an effective solution to support robotic application issues. The interconnection between robotic devices through an FC network can be referred as the Internet of Robotic Things (IoRT). Although the FC network system can provide number of services closer to IoRT devices, it still faces significant challenges including real-time tracing services and a secure tracing services. Therefore, this paper aims to provide a tracking mobile robot devices in a secure and private manner, with high efficiency performance, is considered essential to ensuring the success of IoRT network applications.Design/methodology/approach: This paper proposes a secure anonymous tracing (SAT) method to support the tracing of IoRT devices through a FC network system based on the Counting Bloom filter (CBF) and elliptic curve cryptography techniques. With the proposed SAT mechanism, a fog node can trace a particular robot device in a secure manner, which means that the fog node can provide a service to a particular robot device without revealing any private data such as the device's identity or location.Findings: Analysis shows that the SAT mechanism is both efficient and resilient against tracing attacks. Simulation results are provided to show that the proposed mechanism is beneficial to support IoRT applications over an FC network system.Originality/value: This paper represents a SAT method based on CBF and elliptic curve cryptography techniques as an efficient mechanism that is resilient against tracing attacks.