Qualcomm Incorporated

Sudha Anil Kumar Gathala

Rajarshi Gupta

Saumitra Mohan Das

Vinay Sridhara

Nayeem Islam

Giridhar Dhati Mandyam

Christopher A Vick

Mihai Christodorescu

Mastooreh Salajegheh

Gheorghe Calin Cascaval

Sriram Nandha Premnath

Dallas Wiener

Andrey Ermolinskiy

Harold Gilkey

Xuetao Wei

Minjang Kim

Joel Galenson

Hugo Romero

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.

Methods and systems for on-device real-time adaptive security based on external threat intelligence inputs

Various embodiments include methods for detecting software attacks on a process executing on a computing device. Various embodiment methods may include monitoring structural attributes of a plurality of virtual memory regions utilized by the process, and comparing the monitored structural attributes to the expected structural attributes of the plurality of VMRs. Various embodiment methods may further include determining whether the monitored structural attributes represent anomalous behavior of the process based on the comparison between the monitored structural attributes and the expected structural attributes.

Detecting software attacks on processes in computing devices

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.

Dynamic addition of code in shared libraries

Embodiments include computing devices, apparatus, and methods implemented by the apparatus for implementing wake lock aware scheduling. The apparatus may receive a wake lock request by a wake lock profiler and acquire wake lock information of a wake lock event associated with the wake lock request. The wake lock information may include a wake lock time parameter. The apparatus may send a hint having the wake lock time parameter. The apparatus may receive the hint, determine whether ready jobs can execute during the wake lock event, and send a request for permission to schedule the ready jobs for execution during the wake lock event in response to determining that the ready jobs can execute during the wake lock event.

Wake lock aware system wide job scheduling for energy efficiency on mobile devices

Embodiments include computing devices, apparatus, and methods implemented by the apparatus for time varying address space layout randomization. The apparatus may launch first plurality of versions of a system service and assign a random virtual address space layout to each of the first plurality of versions of the system service. The apparatus may receive a first request to execute the system service from a first application. The apparatus may randomly select a first version of the system service from the first plurality of versions of the system service, and execute the system service using data of the first version of the system service.

Time varying address space layout randomization

Embodiments include computing devices, apparatus, and methods implemented by the apparatus for implementing profile guided indirect jump checking on a computing device, including encountering an indirect jump location of implementing an indirect jump during execution of a program, identifying an indirect jump target of the indirect jump, determining whether the indirect jump location and the indirect jump target are associated in a profile guided indirect jump table, and determining whether the indirect jump location and the indirect jump target are associated in a compiler guided indirect jump table in response to determining that the indirect jump location and the indirect jump target are not associated in the profile guided indirect jump table.

Profile guided indirect function call check for control flow integrity

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.

Data flow based behavioral analysis on mobile devices

Methods and devices for detecting suspicious or performance-degrading mobile device behaviors may include performing behavior monitoring and analysis operations to intelligently, dynamically, and/or adaptively determine the mobile 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 behaviors are to be observed. Such behavior monitoring and analysis operations may be performed continuously (or near continuously) in a mobile device without consuming an excessive amount of processing, memory, or energy resources of the mobile device by identifying hot application programming interfaces (APIs) and hot action patterns that are invoked or used most frequently by software applications of the mobile device and storing information regarding these hot APIs and hot action patterns separately and more efficiently.

Exploiting hot application programming interfaces (APIs) and action patterns for efficient storage of API logs on mobile devices for behavioral analysis

The various aspects include methods, systems, and devices configured to make use of caching techniques and behavior signature caches to improve processor performance and/or reduce the amount of power consumed by the computing device by reducing analyzer latency. The signature caching system may be configured to adapt to rapid and frequent changes in behavioral specifications and models and provide a multi-fold improvement in the scalability of behavioral analysis operations performed on the mobile device.

Methods and systems for minimizing latency of behavioral analysis

Mobile computing devices may be configured to compile and execute portions of a general purpose software application in an auxiliary processor (e.g., a DSP) of a multiprocessor system by reading and writing information to a shared memory. A first process (P1) on the applications processor may request address negotiation with a second process (P2) on the auxiliary processor, obtain a first address map from a first operating system, and send the first address map to the auxiliary processor. The second process (P2) may receive the first address map, obtain a second address map from a second operating system, identify matching addresses in the first and second address maps, store the matching addresses as common virtual addresses, and send the common virtual addresses back to the applications processor. The first and second processes (i.e., P1 and P2) may each use the common virtual addresses to map physical pages to the memory.

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Tracy, CA, United States of America

Sudha Anil Kumar Gathala