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Adaptive malware identification via integrated SimCLR and GRU networks.
Faisal S Alsubaei1, Abdulwahab Ali Almazroi2, Walid Said Atwa2,3
1Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia. fsalsubaei@uj.edu.sa.
Scientific Reports
|July 13, 2025
Summary
This study introduces SimCLR-GRU, an advanced malware detection framework utilizing contrastive learning and recurrent neural networks for enhanced threat identification. It achieves 99% accuracy, offering a robust solution for real-time cybersecurity challenges.
Area of Science:
- Computer Science
- Cybersecurity
- Machine Learning
Background:
- Malware increasingly evades traditional signature-based detection through obfuscation and dynamic behaviors.
- Existing methods exhibit limitations in identifying novel threats, creating vulnerabilities in digital infrastructures.
- The need for adaptive, real-time malware detection systems is critical for enterprise and institutional security.
Purpose of the Study:
- To develop an adaptive and efficient malware detection framework capable of real-time analysis.
- To address the limitations of conventional methods in detecting sophisticated malware.
- To enhance the accuracy and resilience of malware detection systems.
Main Methods:
- Introduced SimCLR-GRU, a novel ensemble architecture combining SimCLR for feature extraction and Gated Recurrent Unit (GRU) for sequential pattern analysis.
- Incorporated Graph Neural Network (GNN)-based feature selection to minimize redundancy.
- Utilized Fish School Search (FSS) for hyperparameter optimization to improve model performance.
Main Results:
- Achieved a classification accuracy of 99% on a Portable Executable (PE) malware dataset, surpassing baseline models by 15%.
- Demonstrated high generalizability and accuracy with an Area Under the Curve (AUC) of 98.2% and an F1-score of 96.8%.
- Reported a low false positive rate of 0.02% and low inference latency, suitable for real-time applications.
Conclusions:
- SimCLR-GRU offers a scalable and effective solution for modern, evolving malware detection challenges.
- The framework's performance highlights its potential for real-time and resource-constrained environments.
- The integration of contrastive learning, GRU, GNN, and FSS provides a robust approach to cybersecurity threats.
