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    Methods and Systems for Using Self-learning Techniques to Protect a Web Application
    2.
    发明申请
    Methods and Systems for Using Self-learning Techniques to Protect a Web Application 审中-公开

    公开(公告)号：US20180020024A1

    公开(公告)日：2018-01-18

    申请号：US15417718

    申请日：2017-01-27

    Applicant: QUALCOMM Incorporated

    Inventor： Hui Chao ,  Nayeem Islam ,  Gheorghe Calin Cascaval

    IPC: H04L29/06 ,  H04L12/26 ,  G06F17/18

    CPC classification number: H04L63/1491 ,  G06F17/18 ,  G06F21/53 ,  G06F21/554 ,  H04L43/0876 ,  H04L63/02 ,  H04L63/1425 ,  H04L67/42

    Abstract: Various embodiments include methods for protecting a web application server from non-benign web application usage. Embodiment methods may include receiving from a client device a service request message that includes information suitable for causing a web application operating on the web application server to perform one or more operations. In response, a processor, such as within the web application server or another network device, may analyze usage of the web application by the client device via a combination of a honeypot component, a sandboxed detonator component, and a Web Application Firewall (WAF) component. Analysis results may be generated by analyzing the received service request message or a server response message sent by the web application server. The analysis results may be used to identify non-benign web application usage. Actions may be taken to protect the web application server and/or the client device from the identified non-benign web application usage.

    DEVICE FOR UAV DETECTION AND IDENTIFICATION
    6.
    发明申请
    DEVICE FOR UAV DETECTION AND IDENTIFICATION 审中-公开

    公开(公告)号：US20170234724A1

    公开(公告)日：2017-08-17

    申请号：US15046390

    申请日：2016-02-17

    Applicant: QUALCOMM Incorporated

    Inventor： Ayman Naguib ,  Nayeem Islam

    IPC: G01H3/08 ,  G01S5/18

    CPC classification number: G01H3/08 ,  G01S5/18 ,  G01S5/186 ,  G01V1/001

    Abstract: Apparatuses and methods are described herein for identifying a Unmanned Aerial Vehicle (UAV), including, but not limited to, determining a first maneuver type, determining a first acoustic signature of sound captured by a plurality of audio sensors while the UAV performs the first maneuver type, determining a second acoustic signature of sound captured by the plurality of audio sensors while the UAV performs a second maneuver type different from the first maneuver type, determining an acoustic signature delta based on the first acoustic signature and the second acoustic signature, and determining an identity of the UAV based on the acoustic signature delta.

    Methods and systems for on-device real-time adaptive security based on external threat intelligence inputs
    8.
    发明授权
    Methods and systems for on-device real-time adaptive security based on external threat intelligence inputs 有权

    公开(公告)号：US10333965B2

    公开(公告)日：2019-06-25

    申请号：US15262858

    申请日：2016-09-12

    Applicant: QUALCOMM Incorporated

    Inventor： Sudha Anil Kumar Gathala ,  Saumitra Mohan Das ,  Nayeem Islam ,  Dallas James Wiener ,  Hugo Romero ,  Harold Gilkey ,  Giridhar Mandyam

    IPC: H04L29/06 ,  G06F21/53 ,  G06F21/57

    Abstract: Methods, and computing devices implementing the methods, that enable client computing devises to work in conjunction with a server device to identify and temporarily defend against non-benign applications (e.g., malware, etc.) and other threats before a more permanent solution or defense (e.g., a patch or software upgrade) becomes available and installed on the client computing device. The server device may be configured to receive reports from the client computing devices, receive threat feeds from third-party servers (e.g., threat intelligence servers, etc.), and use information included in the received threat feed and information included in the received reports to analyze, in the server computing device, a software application that is operating on a client device in multiple passes. The server may generate threat scores (e.g., one for each pass, etc.), and the threat scores to the client computing device for use in devising a customized security response.

    Application characterization for machine learning on heterogeneous core devices
    9.
    发明授权
    Application characterization for machine learning on heterogeneous core devices 有权

    公开(公告)号：US10049327B2

    公开(公告)日：2018-08-14

    申请号：US14680225

    申请日：2015-04-07

    Applicant: QUALCOMM Incorporated

    Inventor： Faraz Mohammad Mirzaei ,  Vinay Sridhara ,  Nayeem Islam

    IPC: G06N99/00 ,  G06F11/34

    Abstract: Methods, devices, systems, and non-transitory process-readable storage media for a computing device to use machine learning to dynamically configure an application and/or complex algorithms associated with the application. An aspect method performed by a processor of the computing device may include operations for performing an application that calls a library function associated with a complex algorithm, obtaining signals indicating user responses to performance of the application, determining whether a user tolerates the performance of the application based on the obtained signals indicating the user responses, adjusting a configuration of the application to improve a subsequent performance of the application in response to determining the user does not tolerate the performance of the application, and storing data indicating the user responses to the performance of the application and other external variables for use in subsequent evaluations of user inputs.

    Context-Based Detection of Anomalous Behavior in Network Traffic Patterns
    10.
    发明申请
    Context-Based Detection of Anomalous Behavior in Network Traffic Patterns 审中-公开

    公开(公告)号：US20180198812A1

    公开(公告)日：2018-07-12

    申请号：US15403477

    申请日：2017-01-11

    Applicant: QUALCOMM Incorporated

    Inventor： Mihai Christodorescu ,  Shuhua Ge ,  Nayeem Islam ,  Hilmi Gunes Kayacik

    IPC: H04L29/06 ,  H04L12/24

    CPC classification number: H04L63/1425 ,  G06F21/552 ,  H04L41/145

    Abstract: Various embodiments provide methods, devices, and non-transitory processor-readable storage media for detecting anomalies in network traffic patterns with a network device by analyzing patterns in network traffic packets traversing the network. Various embodiments include clustering received network traffic packets into groups. The network device receives data packets originating from an endpoint device and analyzes the packets for patterns. The network device may apply a traffic analysis model to the clusters to obtain context classes. The network device may select a behavior classifier model based, at least in part, on the determined context class, and may apply the selected behavior classifier model to determine whether the packet behavior is benign or non-benign.

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