Policy synthesis to enforce group-based policies to unknown flows

    公开(公告)号:US12126535B2

    公开(公告)日:2024-10-22

    申请号:US17498029

    申请日:2021-10-11

    CPC分类号: H04L47/20

    摘要: A system determines a first set of policies, wherein at least one policy entry for a destination role comprises a source role, a traffic attribute, and an action to be taken for the packet. The system represents the policies as a matrix, wherein a first entry in the matrix indicates the source and destination role, the traffic attribute, and the action of the at least one policy entry. The system replaces, in the first entry, the action with the destination role if the action indicates to allow the packet, and with a null value if the action indicates to deny the packet, to obtain a first data structure with entries indicating, for a respective source role, traffic attributes and corresponding sets of allowed destination roles. The system resolves an overlapping pair comprising a first and a second traffic attribute to obtain a second set of synthesized policies.

    SYSTEMS AND METHODS OF APPLYING TENSOR RADIAL BASIS FUNCTION NETWORKS TO MACHINE LEARNING

    公开(公告)号:US20230409665A1

    公开(公告)日:2023-12-21

    申请号:US17857491

    申请日:2022-07-05

    IPC分类号: G06F17/13 G06F17/16

    CPC分类号: G06F17/13 G06F17/16

    摘要: A method of embedding ordinary differential equations (ODEs) into tensor radial basis networks is presented herein. The method involves receiving a tensored basis function having D dimensions and zeroth-, first-, and second-derivative coefficients A_d, B_d, and C_d; defining A_hat, B_hat, and C_hat as a function of A, B, and D, and C_hat as function of A, C, and D, respectively; defining an orthogonal exotic algebra a, b, c; applying a, b, and c, along with A_hat, B_hat, and C_hat, as coefficients for the zeroth-derivative, first-derivative, and second-derivative terms; and embedding the updated tensored basis function by forming a matrix product state (MPS). The MPS can be trained by initializing MPS 3-tensors with random coefficients and sweeping left and right along the MPS and updating the MPS 3-tensors.