Qualcomm Incorporated

Advanced Micro Devices Corporation

Ati Technologies Ulc

Satyajit Prabhakar Patne

Rajarshi Gupta

Vinay Sridhara

Lu Xiao

Mihai Christodorescu

Mark Bapst

Alwyn Dos Remedios

Sumita Rao

Vikram Nair

Stefan Scherer

Robert Abrant

Systems and methods for providing accelerated passphrase verification are disclosed. In one embodiment, a method includes receiving a full security string, generating a full security string hash code, storing the full security string hash code in a memory, determining at least one substring based on an entropy value associated with one or more leading characters in the full security string, generating at least one substring hash code and at least one corresponding character count value, such that the corresponding character count value equals a number of characters in the at least one substring, and storing the at least one substring hash code and the at least one corresponding character count value in the memory.

Accelerated passphrase verification

The various aspects provide a method for recognizing and preventing malicious behavior on a mobile computing device before it occurs by monitoring and modifying instructions pending in the mobile computing device's hardware pipeline (i.e., queued instructions). In the various aspects, a mobile computing device may preemptively determine whether executing a set of queued instructions will result in a malicious configuration given the mobile computing device's current configuration. When the mobile computing device determines that executing the queued instructions will result in a malicious configuration, the mobile computing device may stop execution of the queued instructions or take other actions to preempt the malicious behavior before the queued instructions are executed.

Malware detection and prevention by monitoring and modifying a hardware pipeline

Systems, methods, and devices of the various aspects enable identification of anomalous application behavior by monitoring memory accesses by an application running on a computing device. In various aspects, a level of memory access monitoring may be based on a risk level of an application running on the computing device. The risk level may be determined based on memory address accesses of the application monitored by an address monitoring unit of one or more selected memory hierarchy layers of the computing device. The memory hierarchy layers selected for monitoring for memory address accesses of the application may be based on the determined risk level of the application. Selected memory hierarchy layers may be monitored by enabling one or more address monitoring units (AMUs) associated with the selected one or more memory hierarchy layers. The enabling of selected AMUs may be accomplished by an AMU selection module.

Memory hierarchy monitoring systems and methods

A computing device may use machine learning techniques to determine whether a side channel attack is underway and perform obfuscation operations (e.g., operations to raise the noise floor) or other similar operations to stop or prevent a detected side channel attack. The computing device may determine that a side channel attack is underway in response to determining that the computing device is in airplane mode, that the battery of the computing device the battery has been replaced with a stable DC power supply, that the touch-screen display of the computing device has been disconnected, that there are continuous calls to a cipher application programming interface (API) using the same cipher key, that there has been tampering with a behavioral analysis engine of the computing device, or any combination thereof.

Methods and systems for side channel analysis detection and protection

Various aspects provide methods implemented by at least one processor executing on a mobile communication device to efficiently identify, classify, model, prevent, and/or correct the non-benign (e.g., performance degrading) conditions and/or behaviors that are related to an application operating on the device. Specifically, in various aspects, the mobile computing device may derive or extract application-specific features by performing a binary analysis of an application and may determine the application's category (e.g., a 'games,' 'entertainment,' or &#x201c;news&#x201d; category) based on the application-specific features. The mobile computing device may also obtain a classifier model associated with the application's category that includes various conditions, features, behaviors and corrective actions that may be used to quickly identify and correct non-benign behaviors (e.g., undesirable, malicious, and/or performance-degrading behaviors) occurring on the mobile computing device that are related to the application.

Efficient on-device binary analysis for auto-generated behavioral models

A computing device processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to identify, prevent, correct, and/or otherwise respond to malicious or performance-degrading behaviors of the computing device. As part of these operations, the processor may perform multifactor authentication operations that include determining one or more of a transaction type criticality value, a user confidence value, a software integrity confidence value, and a historical behavior value, using the one or more of these values to determine a number of authentication factors that are be evaluated when authenticating a user of the computing device, and authenticating the user by evaluating the determined number of authentication factors.

Methods and systems for using behavioral analysis towards efficient continuous authentication

A computing device may use machine learning techniques to determine the level, degree, and severity of its vulnerability to side channel attacks. The computing device may intelligently and selectively perform obfuscation operations (e.g., operations to raise the noise floor) to prevent side channel attacks based on the determined level, degree, or severity of its current vulnerability to such attacks. The computing device may also monitor the current level of natural obfuscation produced by the device, determining whether there is sufficient natural obfuscation to prevent a side channel attack during an ongoing critical activity, and perform the obfuscation operation during the ongoing critical activity and in response to determining that there is not sufficient natural obfuscation to adequately protect the computing device against side channel attacks.

Methods and systems for thwarting side channel attacks

Mobile computing devices may be equipped with hardware components configured to monitor key assets of the mobile device at a low level (e.g., firmware level, hardware level, etc.). The hardware component may also be configured to dynamically determine the key assets that are to be monitored in the mobile device, monitor the access or use of these key assets by monitoring data flows, transactions, or operations in a system data bus of the mobile device, and report suspicious activities to a comprehensive behavioral monitoring and analysis system of the mobile device. The comprehensive behavioral monitoring and analysis system may then use this information to quickly identify and respond to malicious or performance degrading activities of the mobile device.

Hardware assisted asset tracking for information leak prevention

A digital rights management system includes an authentication module and a decryption module. If desired, the modules can be implemented in separate integrated circuits. The authentication module retrieves authentication information for protected content and powers down after the authentication information is retrieved. The decryption module decrypts the protected content based on the authentication information while the authentication module is powered down.

Distributed digital rights management system and method

A current mobile multimedia signal time slice is captured using a mobile MM receiver in response to a current mobile multimedia stream-identifying command input. The current mobile multimedia signal time slice contains at least one portion of a currently selected mobile multimedia stream. At least one anticipated mobile multimedia time slice is selectively captured. The at least one anticipated mobile multimedia time slice contains at least one portion of a corresponding anticipated mobile multimedia stream.

Growing community of inventors

San Diego, CA, United States of America

Satyajit Prabhakar Patne