公开(公告)号：US20140153420A1

公开(公告)日：2014-06-05

申请号：US13759844

申请日：2013-02-05

Applicant: QUALCOMM INCORPORATED

Inventor： Lionel Jacques Garin ,  Vinay Sridhara ,  Xiaoxin Zhang ,  Sundar Raman ,  Gengsheng Zhang ,  Bernal A. Arroyo ,  Carlos Horacio Aldana

IPC: H04W24/00

CPC classification number: H04W24/00 ,  H04B7/0671 ,  H04B7/0828 ,  H04B7/0874

Abstract: Systems, apparatus and methods for determining a cyclic shift delay (CSD) mode from a plurality of CSD modes is disclosed. A received OFDM signal is converted to a channel impulse response (CIR) signal in the time domain and/or a channel frequency response (CFR) signal in the frequency domain. Matched filters and a comparator are used to determine a most likely current CSD mode. Alternatively, a classifier is used with a number of inputs including outputs from two or more matched filters and one or more outputs from a feature extractor. The feature extractor extracts features in the time domain from the CIR signal and/or in the frequency domain from the CFR signal useful in distinguishing various CSD modes.

Abstract translation: 公开了用于从多个CSD模式确定循环移位延迟（CSD）模式的系统，装置和方法。 接收到的OFDM信号在时域中被转换为信道脉冲响应（CIR）信号和/或频域中的信道频率响应（CFR）信号。 匹配滤波器和比较器用于确定最可能的当前CSD模式。 或者，分类器使用多个输入，包括来自两个或多个匹配滤波器的输出和来自特征提取器的一个或多个输出。 特征提取器从CIR信号和/或频域中提取时域中的特征，来自用于区分各种CSD模式的CFR信号。

公开(公告)号：US20140053260A1

公开(公告)日：2014-02-20

申请号：US13923547

申请日：2013-06-21

Applicant: QUALCOMM Incorporated

Inventor： Rajarshi Gupta ,  Vinay Sridhara ,  Anil Gathala ,  Xuetao Wei

IPC: G06F21/50

CPC classification number: G06F21/50 ,  G06F21/316 ,  G06F21/552

Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources.

公开(公告)号：US20130304676A1

公开(公告)日：2013-11-14

申请号：US13773247

申请日：2013-02-21

Applicant: QUALCOMM INCORPORATED

Inventor： Rajarshi Gupta ,  Xuetao Wei ,  Anil Gathala ,  Vinay Sridhara

IPC: G06N99/00

CPC classification number: G06N99/005 ,  G06N5/043

Abstract: Methods, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign.

Abstract translation: 用于在通信系统中生成数据模型的方法，系统和设备可以包括应用机器学习技术来生成使用加强的决策树来描述行为矢量语料库的分类器模型的第一族。 可以使用这样的行为矢量来计算升压决策树的一个或多个节点的权重值。 可以识别分类器模型的因素，其可能基于所计算的权重值来确定移动设备行为是良性还是不良性。 用于升压的决策树节点的计算权重值可以包括计算生成的升压决策树节点的独占应答比率。 识别的因素可以应用于行为矢量语料库以产生第二类分类器模型，其识别与使移动设备能够确定行为是良性还是不良性相关的较少因素和数据点。

公开(公告)号：US10021123B2

公开(公告)日：2018-07-10

申请号：US14753666

申请日：2015-06-29

Applicant: QUALCOMM Incorporated

Inventor： Saumitra Mohan Das ,  Mona Mahmoudi ,  Vinay Sridhara ,  Rajarshi Gupta ,  Yin Chen

IPC: H04L29/06 ,  H04L12/26 ,  G06F21/55 ,  H04L12/24

CPC classification number: H04L63/1425 ,  G06F21/552 ,  H04L41/0631 ,  H04L43/0817 ,  H04L63/1416

Abstract: Systems, methods, and devices of the various aspects enable identification of anomalous application behavior. A computing device processor may detect network communication activity of an application on the computing device. The processor may identify one or more device states of the computing device, and one or more categories of the application. The processor may determine whether the application is behaving anomalously based on a correlation of the detected network communication activity of the application, the identified one or more device states of the computing device, and the identified one or more categories of the application.

公开(公告)号：US09979606B2

公开(公告)日：2018-05-22

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: G06F15/173 ,  H04L12/24 ,  H04L12/26 ,  H04L12/28

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.

公开(公告)号：US09910984B2

公开(公告)日：2018-03-06

申请号：US14837936

申请日：2015-08-27

Applicant: QUALCOMM Incorporated

Inventor： Andres Valencia ,  Vinay Sridhara ,  Yin Chen ,  Rajarshi Gupta

IPC: G06F21/55 ,  G06F21/57 ,  G06N99/00 ,  G06F21/56

CPC classification number: G06F21/554 ,  G06F21/55 ,  G06F21/566 ,  G06F21/577 ,  G06F2221/034 ,  G06N99/005

Abstract: Various aspects include methods and computing devices implementing the methods for evaluating device behaviors in the computing devices. Aspect methods may include using a behavior-based machine learning technique to classify a device behavior as one of benign, suspicious, and non-benign. Aspect methods may include using one of a multi-label classification and a meta-classification technique to sub-classify the device behavior into one or more sub-categories. Aspect methods may include determining a relative importance of the device behavior based on the sub-classification, and determining whether to perform robust behavior-based operations based on the determined relative importance of the device behavior.

公开(公告)号：US09875357B2

公开(公告)日：2018-01-23

申请号：US14616046

申请日：2015-02-06

Applicant: QUALCOMM Incorporated

Inventor： Yin Chen ,  Man Ki Yoon ,  Vinay Sridhara

IPC: G06F21/00 ,  G06F12/14 ,  G06F21/56 ,  H04L29/06 ,  G06F21/31

CPC classification number: G06F21/566 ,  G06F21/316 ,  G06F2221/2133

Abstract: A computing device processor may be configured with processor-executable instructions to implement methods of detecting and responding to fake user interaction (UI) events. The processor may determine whether a user interaction event is a fake user interaction event by analyzing raw data generated by one or more hardware drivers in conjunction with user interaction event information generated or received by the high-level operating system. In addition, the processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to identify, prevent, correct, or otherwise respond to malicious or performance-degrading behaviors of the computing device based on whether a detected user interaction event is an authentic or fake user interaction event.

公开(公告)号：US09736638B2

公开(公告)日：2017-08-15

申请号：US14684112

申请日：2015-04-10

Applicant: QUALCOMM Incorporated

Inventor： Behrooz Khorashadi ,  Sameera Poduri ,  Vinay Sridhara ,  Payam Pakzad

IPC: H04W4/04 ,  H04W4/02 ,  H04W64/00 ,  G01C21/20

CPC classification number: H04W4/023 ,  G01C21/005 ,  G01C21/206 ,  H04W4/025 ,  H04W4/027 ,  H04W4/04 ,  H04W64/00

Abstract: In one implementation, a method may comprise: storing a user profile indicative of at least one attribute of a user of a mobile station; determining a measurement value based, at least in part, on a signal from at least one sensor on the mobile station; and estimating a location of the mobile station based, at least in part, on an association of the at least one attribute and the measurement value with a context parameter map database.

公开(公告)号：US09652362B2

公开(公告)日：2017-05-16

申请号：US14259501

申请日：2014-04-23

Applicant: QUALCOMM Incorporated

Inventor： Rajarshi Gupta ,  Alexander Gantman ,  Vinay Sridhara

IPC: G06F11/00 ,  G06F11/36 ,  H04L29/06 ,  G06N5/04 ,  G06F21/55 ,  G06F21/56 ,  G06F21/44 ,  G06F21/52 ,  G06N99/00 ,  G06F19/00

CPC classification number: G06F11/3612 ,  G06F19/707 ,  G06F21/44 ,  G06F21/52 ,  G06F21/552 ,  G06F21/562 ,  G06F21/566 ,  G06F2221/034 ,  G06N5/04 ,  G06N5/043 ,  G06N99/005 ,  H04L63/14

Abstract: Methods, and mobile devices implementing the methods, use application-specific and/or application-type specific classifier to improve the efficiency and performance of a comprehensive behavioral monitoring and analysis system predicting whether a software application is causing undesirable or performance depredating behavior. The application-specific and application-type specific classifier models may include a reduced and more focused subset of the decision nodes that are included in a full or more complete classifier model that may be received or generated in the mobile device. The locally generated application-specific and/or application-type specific classifier models may be used to perform real-time behavior monitoring and analysis operations by applying the application-based classifier models to a behavior/feature vector generated by monitoring mobile device behavior. The various aspects focus monitoring and analysis operations on a small number of features that are most important for determining whether operations of a software application are contributing to undesirable or performance depredating behavior.

公开(公告)号：US20160379136A1

公开(公告)日：2016-12-29

申请号：US14751572

申请日：2015-06-26

Applicant: QUALCOMM Incorporated

Inventor： Yin Chen ,  Vinay Sridhara ,  Mihai Christodorescu

IPC: G06N99/00

CPC classification number: G06N20/00 ,  G06F21/552 ,  G06F21/566

Abstract: An aspect computing device may be configured to perform program analysis operation in response to classifying a behavior as non-benign. The program analysis operation may identify new sequences of API calls or activity patterns that are associated with the identified non-benign behaviors. The computing device may learn new behavior features based on the program analysis operation or update existing behavior features based on the program analysis operation. For example, API sequences observed to occur when a non-benign behavior is recognized may be added to behavior features observed during program analysis operation.

Abstract translation: 方面计算设备可以被配置为响应于将行为分类为非良性来执行程序分析操作。 程序分析操作可以识别与所识别的非良性行为相关联的API调用或活动模式的新序列。 计算设备可以基于程序分析操作学习新的行为特征，或者基于程序分析操作来更新现有行为特征。 例如，当识别到非良性行为时观察到发生的API序列可以被添加到在程序分析操作期间观察到的行为特征。