Abstract: A system for detecting malware includes a server and processor that executes instructions to receive, from the server, a first malware detection model and a database of known malicious files; label each file of a training data set as either malicious or clean by comparing each file of the training data set to the database, where if a match is not found to in the database, the model is used to predict maliciousness; train the first malware detection model using the labeled training data set; transmit parameters of the trained first malware detection model to the server; and receive, from the server, a second malware detection model, wherein the second malware detection model is trained by federated learning using the parameters of the trained first malware detection model and additional parameters provided by one or more remote devices.

Abstract: Systems and methods for computer security are provided by a processor programmed to: receive an Internet file and produce a cryptographic hash of the Internet file; compare the cryptographic hash to external malware databases and external antiviral databases for a malicious file match to determine the Internet file's status that is based upon a weighted consensus algorithm derived from the external malware databases and the external antiviral databases; check if the Internet file's status determination matches the internal malicious software database Internet file's status and update the internal malicious software database based upon the Internet file's status determination if a threshold for the weighted consensus algorithm is exceeded; and train a machine learning algorithm using the Internet file's status determination to create a labelled data set based upon the Internet file's status determination, and provide a report via the input/output device based upon the Internet file's status determination.

Abstract: A method of generating a predictive model for malware detection using federated learning includes transmitting, to each of a plurality of remote devices, a copy of the predictive model, where the predictive model is configured to predict whether a file is malicious; receiving, from each of the plurality of remote devices, model parameters determined by independently training the copy of the predictive model on each of the plurality of remote devices using local files stored on respective ones of the plurality of remote devices; generating a federated model by training the predictive model based on the model parameters received from each of the plurality of remote devices; and transmitting the federated model to each of the plurality of remote devices.