公开(公告)号：US12099599B2

公开(公告)日：2024-09-24

申请号：US17538703

申请日：2021-11-30

Applicant: Huawei Technologies Co., Ltd.

Inventor： Moez Draief ,  Xiang Chen ,  Konstantin Kutzkov ,  Kevin Scaman ,  Milan Vojnovic

IPC: G06F11/00 ,  G06F12/14 ,  G06F12/16 ,  G06F21/55 ,  G06N3/044 ,  G08B23/00

CPC classification number: G06F21/552 ,  G06N3/044 ,  G06F2221/033

Abstract: Apparatuses and methods for determining if a computer program is malware and to which malware class it belongs to. In the method, the behaviour of a computer program is traced by observing the activity of the program. Behaviour sequences comprising API-calls or similar activity of a computer program are then provided into a classifier for classifying the computer program. From the outcome of the classifier, a classification result and the portions relevant to decision can be provided to a person for further confirmation.

公开(公告)号：US20230385615A1

公开(公告)日：2023-11-30

申请号：US18365047

申请日：2023-08-03

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Aladin VIRMAUX ,  George Dasoulas ,  Kevin Scaman

IPC: G06N3/048

CPC classification number: G06N3/048

Abstract: Described is a data processing device for performing an attention-based operation on a graph neural network. The device is configured to receive one or more input graphs each having a plurality of nodes and to, for at least one of the input graphs: form an input node representation for each node in the respective input graph, wherein a respective norm is defined for each input node representation; form a set of attention parameters; multiply each of the input node representations with each of the set of attention parameters to form a score function of the respective input graph; normalize the score function based on a maximum of the norms of the input node representations to form a normalised score function; and form a weighted node representation by weighting each node in the respective input graph using a respective element of the normalised score function. The normalization of the score function enables deep attention-based neural networks to perform better by enforcing Lipschitz continuity.