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Over past years, the manually methods to create detection rules were nolonger practical in the anti-malware product since the number of malwarethreats has been growing. Thus, the turn to the machine learning approaches isa promising way to make the malware recognition more efficient. The traditionalcentralized machine learning requires a large amount of data to train a modelwith excellent performance. To boost the malware detection, the training datamight be on various kind of data sources such as data on host, network andcloud-based anti-malware components, or even, data from different enterprises.To avoid the expenses of data collection as well as the leakage of privatedata, we present a federated learning system to identify malwares through thebehavioural graphs, i.e., system call dependency graphs. It is based on a deeplearning model including a graph autoencoder and a multi-classifier module.This model is trained by a secure learning protocol among clients to preservethe private data against the inference attacks. Using the model to identifymalwares, we achieve the accuracy of 85\% for the homogeneous graph data and93\% for the inhomogeneous graph data.

Symbolic analysis meets federated learning to enhance malware identifier