Title: Imbalanced Malware Images Classification: a CNN based Approach,
Abstract: Deep convolutional neural networks (CNNs) can be applied to malware binary
detection through images classification. The performance, however, is degraded
due to the imbalance of malware families (classes). To mitigate this issue, we
propose a simple yet effective weighted softmax loss which can be employed as
the final layer of deep CNNs. The original softmax loss is weighted, and the
weight value can be determined according to class size. A scaling parameter is
also included in computing the weight. Proper selection of this parameter has
been studied and an empirical option is given. The weighted loss aims at
alleviating the impact of data imbalance in an end-to-end learning fashion. To
validate the efficacy, we deploy the proposed weighted loss in a pre-trained
deep CNN model and fine-tune it to achieve promising results on malware images
classification. Extensive experiments also indicate that the new loss function
can fit other typical CNNs with an improved classification performance.