Cryptomining malware detection based on edge computing-oriented multi-modal features deep learning

In recent years, with the increase in the price of cryptocurrencies, the number of malicious cryptomining software has increased significantly. With their powerful spreading ability, cryptomining malware can unknowingly occupy our resources, harm our interests, and damage more legitimate assets. However, although current traditional rule-based malware detection methods have a low false alarm rate, they have a relatively low detection rate when faced with a large volume of emerging malware. Even though common machine learning-based or deep learning-based methods have certain ability to learn and detect unknown malware, the characteristics they learn are single and independent, and cannot be learned adaptively. Aiming at the above problems, we propose a deep learning model with multi-input of multi-modal features, which can simultaneously accept digital features and image features on different dimensions. The model in turn includes parallel learning of three sub-models and ensemble learning of another specific sub-model. The four sub-models can be processed in parallel on different devices and can be further applied to edge computing environments. The model can adaptively learn multi-modal features and output prediction results. The detection rate of our model is as high as 97.01% and the false alarm rate is only 0.63%. The experimental results prove the advantage and effectiveness of the proposed method.