Transfer Learning in Cybersecurity: Enhancing Malware Classification and Detection

Abstract

The growing sophistication of malware poses significant challenges to cybersecurity professionals. Traditional machine learning approaches in malware classification often require vast amounts of labeled data, which may not always be available. Transfer learning, a technique where knowledge gained from one task is applied to another, presents a promising solution to enhance malware detection and classification. This paper explores the application of transfer learning in cybersecurity, specifically focusing on its role in improving malware classification and detection. It discusses various transfer learning methods, their effectiveness in leveraging pre-trained models, and case studies demonstrating their application in real-world scenarios. The findings suggest that transfer learning can significantly improve the accuracy and efficiency of malware detection systems, thus enhancing the overall security posture of organizations.