US 11,775,642 B1
Malware detection using federated learning
Mantas Briliauskas, Vilnius (LT); and Dainius Razinskas, Vilnius (LT)
Assigned to UAB 360 IT, Vilnius (LT)
Filed by UAB 360 IT, Vilnius (LT)
Filed on Jun. 17, 2022, as Appl. No. 17/843,318.
Application 17/843,318 is a continuation of application No. 17/843,062, filed on Jun. 17, 2022, granted, now 11,593,485.
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 21/56 (2013.01); G06N 20/00 (2019.01)
CPC G06F 21/566 (2013.01) [G06F 21/565 (2013.01); G06N 20/00 (2019.01); G06F 2221/034 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A malware detection method that uses federated learning, the method comprising:
transmitting, to a remote device, file characterization information for one or more local files, wherein the remote device generates a labeled training data set by: i) comparing the file characterization information of each of the one or more local files to a malware properties database, and ii) labeling each of the one or more files as either malicious or clean based on the comparison;
receiving, from the remote device, a first malware detection model and the labeled training data set;
training the first malware detection model using the labeled training data set;
transmitting parameters of the trained first malware detection model to the remote device; and
receiving, from the remote device, 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 additional remote devices.