A Few-Shot Meta-Learning based Siamese Neural Network using Entropy Features for Ransomware Classification
This addresses the need for rapid ransomware detection and classification in cybersecurity, though it is incremental as it builds on existing meta-learning and Siamese network techniques.
The paper tackled the problem of ransomware classification with limited data by proposing a few-shot meta-learning based Siamese Neural Network using entropy features, achieving a weighted F1-score exceeding 86%.
Ransomware defense solutions that can quickly detect and classify different ransomware classes to formulate rapid response plans have been in high demand in recent years. Though the applicability of adopting deep learning techniques to provide automation and self-learning provision has been proven in many application domains, the lack of data available for ransomware (and other malware)samples has been raised as a barrier to developing effective deep learning-based solutions. To address this concern, we propose a few-shot meta-learning based Siamese Neural Network that not only detects ransomware attacks but is able to classify them into different classes. Our proposed model utilizes the entropy feature directly extracted from ransomware binary files to retain more fine-grained features associated with different ransomware signatures. These entropy features are used further to train and optimize our model using a pre-trained network (e.g. VGG-16) in a meta-learning fashion. This approach generates more accurate weight factors, compared to feature images are used, to avoid the bias typically associated with a model trained with a limited number of training samples. Our experimental results show that our proposed model is highly effective in providing a weighted F1-score exceeding the rate>86% compared