Symbolic analysis meets federated learning to enhance malware identifier
share
Over past years, the manually methods to create detection rules were no longer practical in the anti-malware product since the number of malware threats has been growing. Thus, the turn to the machine learning approaches is a promising way to make the malware recognition more efficient. The traditional centralized machine learning requires a large amount of data to train a model with excellent performance. To boost the malware detection, the training data might be on various kind of data sources such as data on host, network and cloud-based anti-malware components, or even, data from different enterprises. To avoid the expenses of data collection as well as the leakage of private data, we present a federated learning system to identify malwares through the behavioural graphs, i.e., system call dependency graphs. It is based on a deep learning model including a graph autoencoder and a multi-classifier module. This model is trained by a secure learning protocol among clients to preserve the private data against the inference attacks. Using the model to identify malwares, we achieve the accuracy of 85% for the homogeneous graph data and 93% for the inhomogeneous graph data.
READ FULL TEXT
