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Rapid digitalisation spurred by the Covid-19 pandemic has resulted in morecyber crime. Malware-as-a-service is now a booming business for cybercriminals. With the surge in malware activities, it is vital for cyberdefenders to understand more about the malware samples they have at hand assuch information can greatly influence their next course of actions during abreach. Recently, researchers have shown how malware family classification canbe done by first converting malware binaries into grayscale images and thenpassing them through neural networks for classification. However, most workfocus on studying the impact of different neural network architectures onclassification performance. In the last year, researchers have shown thataugmenting supervised learning with self-supervised learning can improveperformance. Even more recently, Data2Vec was proposed as a modality agnosticself-supervised framework to train neural networks. In this paper, we presentBinImg2Vec, a framework of training malware binary image classifiers thatincorporates both self-supervised learning and supervised learning to produce amodel that consistently outperforms one trained only via supervised learning.We were able to achieve a 4% improvement in classification performance and a0.5% reduction in performance variance over multiple runs. We also show how ourframework produces embeddings that can be well clustered, facilitating modelexplanability.

BinImg2Vec: Augmenting Malware Binary Image Classification with Data2Vec