ISAnWin: inductive generalized zero-shot learning using deep CNN for malware detection across windows and android platforms.

ABSTRACT: Effective malware detection is critical to safeguarding digital ecosystems from evolving cyber threats. However, the scarcity of labeled training data, particularly for cross-family malware detection, poses a significant challenge. This research proposes a novel architecture ConvNet-6 to be used in Siamese Neural Networks for applying Zero-shot learning to address the issue of data scarcity. The proposed model for malware detection uses the ConvNet-6 architecture even with limited training samples. The proposed model is trained with just one labeled sample per sub-family. We conduct extensive experiments on a diverse dataset featuring Android and Portable Executables' malware families. The model achieves high performance in terms of 82% accuracy on the test dataset, demonstrating its abili

SUBMITTER: Tayyab UE 

PROVIDER: S-EPMC11784898 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature