Efficient Malware Detection Using Transfer Learning and GAN-Based Data Augmentation

Rapid malware evolution calls for the creation of novel, imaginative techniques that enhance the accuracy and resilience of traditional detection methods. Here, a hybrid strategy focused on malware detection will be proposed, where GANs and transfer learning are employed to improve the accuracy and resilience of image representations. Converting malware binaries into grayscale images may change how visual features are used to identify specific patterns that could define malware and eventually determine classification. GANs are used to generate artificial malware samples, which enhance the dataset with a variety of adversarial examples that closely mimic the diversity of actual malware. The model is therefore more resistant to deceptive infection techniques. Convolutional neural network pre-training enables transfer learning and saves resources for lengthy training from scratch and decreases the strain of training. As was previously said, the GAN-generative adversarial examples feature a wider variety of malware types that enhance its capacity to identify new threats precisely. Opposing the traditional approach, the experimental findings showed that this hybrid strategy improved the detection accuracy and decreased the rate of false positives. The reason for this is its adaptability and scalability, which qualify it for real-time malware detection.