Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware

• URL: http://arxiv.org/abs/2110.13409v3
• Date: Thu, 15 Jun 2023 02:30:53 GMT
• Title: Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware
• Authors: Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh, Paul A. Watters, Seyit Camtepe
• Abstract summary: Existing malware detection methods fail to correctly classify different malware families when obfuscated malware samples are present in the training dataset. We propose a novel task-aware few-shot-learning-based Siamese Neural Network that is resilient against such control flow obfuscation techniques. Our proposed approach is highly effective in recognizing unique malware signatures, thus correctly classifying malware samples that belong to the same malware family.
• Score: 5.293553970082943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware families when such obfuscated malware samples are present in the training dataset, resulting in high false-positive rates. To address this issue, we propose a novel task-aware few-shot-learning-based Siamese Neural Network that is resilient against the presence of malware variants affected by such control flow obfuscation techniques. Using the average entropy features of each malware family as inputs, in addition to the image features, our model generates the parameters for the feature layers, to more accurately adjust the feature embedding for different malware families, each of which has obfuscated malware variants. In addition, our proposed method can classify malware classes, even if there are only one or a few training samples available. Our model utilizes few-shot learning with the extracted features of a pre-trained network (e.g., VGG-16), to avoid the bias typically associated with a model trained with a limited number of training samples. Our proposed approach is highly effective in recognizing unique malware signatures, thus correctly classifying malware samples that belong to the same malware family, even in the presence of obfuscated malware variants. Our experimental results, validated by N-way on N-shot learning, show that our model is highly effective in classification accuracy, exceeding a rate \textgreater 91\%, compared to other similar methods.

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