ItemConference Paper

Hybrid GNN-Driven Framework for Intelligent Malware Detection and Cryptojacking Prevention in Heterogeneous Cloud Environments

Title
Hybrid GNN-Driven Framework for Intelligent Malware Detection and Cryptojacking Prevention in Heterogeneous Cloud Environments
Creator
Aarthee, S.; Palanimeera, J.; Anu, P.; Saseekala, M.; Subhashini, A.
Description
Cloud environments are increasingly targeted by cryptojackers who use the computers processing capabilities for mining cryptocurrency without authorization. This research aims to enhance the security features that protect against cyber attackers by implementing deep learning techniques that help to detect anomalous behaviors in the cloud through analysis of data from typical system transactions. The hybrid HGCN-SIEM Fusion architecture for cryptojacking prevention and malware detection incorporates four types of Graph Neural Network (GNN) approaches: GCN, GAT, GIN, and GraphSAGE. The proposed technique achieves superior malware detection accuracy compared to all baseline models. After experiments on the standard SoK cryptojacking malware dataset, GAT and GraphSAGE demonstrated an accuracy average of 97.5%, GCN and GIN achieved similar accuracy, with an average score of 95.5%. The HGCN-SIEM model outperforms with an optimum accuracy of 98.8%, ensures low latency, and provides a well-balanced mix of rapid attack detection and the best utilization of the network bandwidth. SHA-256 is used to hash all process, instance, and event identifiers to protect privacy and ensure distinct, impenetrable node representations. Graph sampling, edge pruning, and adaptive batching are used to manage computational scalability in heterogeneous cloud networks, which reduces latency, increases throughput, and optimizes resource utilization during inference. This research work points out those GNN architectures that combine different node types that are extremely useful for security monitoring and malware detection in various network settings, demonstrating reliability and practicality in cybersecurity contexts. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Source
Lecture Notes in Networks and Systems;Volume;1937 LNNS;pp.326-344
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Cloud security; Cryptojacking; Graph neural networks; Machine learning; Malware detection; Network security
Coverage
Aarthee S., School of Computing, SASTRA Deemed to be University, Tamil Nadu, Thanjavur, India; Palanimeera J., Sathyabama Institute of Science and Technology, Tamil Nadu, Chennai, India; Anu P., School of Computing, SASTRA Deemed to be University, Tamil Nadu, Thanjavur, India; Saseekala M., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Subhashini A., Department of Software Systems, PSG College of Arts and Science, Tamil Nadu, Coimbatore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-303223576-3;
Format
online
Language
English
Type
Conference paper

Citation

Aarthee, S.; Palanimeera, J.; Anu, P.; Saseekala, M.; Subhashini, A., “Hybrid GNN-Driven Framework for Intelligent Malware Detection and Cryptojacking Prevention in Heterogeneous Cloud Environments,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25420.

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