A Few-Shot Meta-Learning based Siamese Neural Network using Entropy Features for Ransomware Classification

Abstract

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

References in corpus (5)

• Early Stage Malware Prediction Using Recurrent Neural Networks
• Imbalanced Malware Images Classification: a CNN based Approach
• Leveraging Support Vector Machine for Opcode Density Based Detection of Crypto-Ransomware
• Differential Area Analysis for Ransomware Attack Detection within Mixed File Datasets
• Deep Transfer Learning for Static Malware Classification

Cited by in corpus (5)

• IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset
• Data-Centric Machine Learning Approach for Early Ransomware Detection and Attribution
• CanCal: Towards Real-time and Lightweight Ransomware Detection and Response in Industrial Environments
• Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware
• Differential Area Analysis for Ransomware: Attacks, Countermeasures, and Limitations