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公开(公告)号:US20210248266A1
公开(公告)日:2021-08-12
申请号:US15733777
申请日:2019-03-19
Inventor: Fabio GIUBILO , Fadi EL-MOUSSA , Mark SHACKLETON
Abstract: A computer implemented method of sharing a data message containing multiple data fields between a provider computer system and a consumer computer system, wherein the provider and consumer computer systems have mutual mistrust, is disclosed.
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公开(公告)号:US20170230835A1
公开(公告)日:2017-08-10
申请号:US15503567
申请日:2015-07-28
Inventor: Fabrice SAFFRE , Anvar TUKMANOV , Mark SHACKLETON , Richard MACKENZIE
IPC: H04W16/06
CPC classification number: H04W16/06 , H04W16/32 , H04W84/045
Abstract: This disclosure provides a method of allocating a resource in a network of small cells, and a device for implementing the method, the method comprising: a first small cell detecting that its resource demand exceeds its resource allocation; the first small cell selecting a new resource that is being used by a second small cell; and the first small cell allocating the new resource to either the first or second small cell, wherein the probability the new resource is allocated to the first small cell is based on the first small cell's resource demand
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公开(公告)号:US20210182404A1
公开(公告)日:2021-06-17
申请号:US16762284
申请日:2018-10-11
Inventor: Mark SHACKLETON , Fadi EL-MOUSSA
Abstract: A computer implemented method to generate training data for a machine learning algorithm for determining security vulnerabilities of a virtual machine (VM) in a virtualized computing environment is disclosed. The machine learning algorithm determines the vulnerabilities based on a vector of configuration characteristics for the VM.
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公开(公告)号:US20210182403A1
公开(公告)日:2021-06-17
申请号:US16762283
申请日:2018-10-11
Inventor: Mark SHACKLETON , Fadi EL-MOUSSA
Abstract: A computer implemented method to determine a security configuration for a target virtual machine (VM) in a virtualized computing environment, the method including training a machine learning algorithm to determine a vector of security vulnerabilities for the target VM based on a vector of configuration characteristics for the target VM, the machine learning algorithm being trained using training examples each including a configuration for a training VM and an associated vulnerability vector based on an observed security occurrence at the training VM, wherein each training example further includes an identification of one of set of security configurations for the training VM; selecting at least a subset of the set of security configurations and, for each security configuration in the subset, executing the machine learning algorithm with the vector of configuration characteristics for the target VM and an identification of the security configuration, so as to generate a set of vulnerability vectors including a vulnerability vector for each security configuration in the selected subset; and selecting a security configuration for the target VM based on the set of vulnerability vectors.
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公开(公告)号:US20180063726A1
公开(公告)日:2018-03-01
申请号:US15562189
申请日:2016-03-23
Inventor: Mark SHACKLETON , Fabrice SAFFRE , Anvar TUKMANOV , Richard MACKENZIE
CPC classification number: H04W24/02 , H04W4/029 , H04W36/0083 , H04W36/04 , H04W40/08 , H04W52/34 , H04W52/367 , H04W84/045 , H04W88/08 , Y02D70/124 , Y02D70/1262 , Y02D70/164 , Y02D70/22 , Y02D70/324
Abstract: In a wireless network formed of short range femtocells, each femtocell provides wireless connectivity to user equipment devices and the user equipment can move around the topographical range covered by the network by handing over to a neighboring femtocell. Due to the limited range of a femtocell, there will be coverage gaps. If the device moves to a location not covered by a femtocell, it will try to connect to a macrocell of a different wide area cellular network until it is within range of another femtocell network. To minimize handovers from the femtocell network to the macrocell network, each femtocell is arranged to analyze historic log data to detect coverage gaps experienced by the user equipment as it moves along a user equipment route and try to close the gaps by increasing the coverage range of femtocells on either side of the coverage gap to close the gap.
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