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Matthew Gaber: Peekaboo Transformer Models

Authors :
Gaber, Matthew G.
Ahmed, Mohiuddin
Janicke, Helge
Gaber, Matthew G.
Ahmed, Mohiuddin
Janicke, Helge
Source :
Research Datasets
Publication Year :
2024

Abstract

Finding automated AI techniques to proactively defend against malware has become increasingly critical. The ability of an AI model to correctly classify novel malware is dependent on the quality of the features it is trained with. In turn, the authenticity and quality of the features is dependent on the analysis tool and the dataset. Peekaboo, a Dynamic Binary Instrumentation tool defeats evasive malware to capture its genuine behavior. Transformer models trained with Peekaboo data excel in detecting new malicious functions, outperforming prior approaches in novel ransomware detection.This dataset contains the fine tuned models and the Colab scripts used for training and testing.

Details

Database :
OAIster
Journal :
Research Datasets
Notes :
Research Datasets
Publication Type :
Electronic Resource
Accession number :
edsoai.on1452785895
Document Type :
Electronic Resource