公开(公告)号：US20170286066A1

公开(公告)日：2017-10-05

申请号：US15085415

申请日：2016-03-30

Applicant: QUALCOMM Incorporated

Inventor： Sudha Anil Kumar GATHALA ,  Mihai CHRISTODORESCU ,  Mastooreh SALAJEGHEH

IPC: G06F9/44

CPC classification number: G06F8/30 ,  G06F8/54 ,  G06F9/44521 ,  G06F9/44557 ,  G06F11/3466 ,  G06F11/3624 ,  G06F11/3644

Abstract: Various embodiments include methods for dynamically modifying shared libraries on a client computing device. Various embodiment methods may include receiving a first set of code segments and a first set of code sites associated with a first application. Each code in the first set of code sites may include an address within a compiled shared library stored on the client computing device. The compiled shared library may include one or more dummy instructions inserted at each code site in the first set of code sites, and each code segment in the first set of code segments may be associated with a code site in the first set of code sites. The client computing device may insert each code segment in the first set of code segments at its associated code site in the compiled shared library.

公开(公告)号：US20160261465A1

公开(公告)日：2016-09-08

申请号：US14638602

申请日：2015-03-04

Applicant: QUALCOMM Incorporated

Inventor： Rajarshi GUPTA ,  Mastooreh SALAJEGHEH ,  Mihai CHRISTODORESCU ,  Vinay SRIDHARA ,  Govindarajan KRISHNAMURTHI

IPC: H04L12/24 ,  H04L12/26

CPC classification number: H04L41/14 ,  H04L12/2816 ,  H04L41/0654 ,  H04L41/12 ,  H04L41/145 ,  H04L41/5061 ,  H04L43/04 ,  H04L43/08

Abstract: The disclosure generally relates to behavioral analysis to automate monitoring Internet of Things (IoT) device health in a direct and/or indirect manner. In particular, normal behavior associated with an IoT device in a local IoT network may be modeled such that behaviors observed at the IoT device may be compared to the modeled normal behavior to determine whether the behaviors observed at the IoT device are normal or anomalous. Accordingly, in a distributed IoT environment, more powerful “analyzer” devices can collect behaviors locally observed at other (e.g., simpler) “observer” devices and conduct behavioral analysis across the distributed IoT environment to detect anomalies potentially indicating malicious attacks, malfunctions, or other issues that require customer service and/or further attention. Furthermore, devices with sufficient capabilities may conduct (local) on-device behavioral analysis to detect anomalous conditions without sending locally observed behaviors to another aggregator device and/or analyzer device.

Abstract translation: 本公开通常涉及以直接和/或间接方式自动监视物联网（IoT）设备健康状况的行为分析。 特别地，可以建模与本地IoT网络中的IoT设备相关联的正常行为，使得可以将在IoT设备处观察到的行为与建模的正常行为进行比较，以确定在IoT设备处观察到的行为是正常还是异常。 因此，在分布式IoT环境中，更强大的“分析器”设备可以收集在其他（例如更简单的）“观察者”设备本地观察到的行为，并在分布式IoT环境中进行行为分析，以检测潜在地指示恶意攻击，故障或 其他需要客户服务和/或进一步关注的问题。 此外，具有足够能力的设备可以进行（本地）设备上行为分析以检测异常情况，而不将本地观察到的行为发送到另一聚合器设备和/或分析仪设备。