Disclosed are various embodiments for controlling access to resources in a network environment. Methods may include installing a profile on the device and installing a certificate included in or otherwise associated with the profile on the device. A request to execute an application, and/or access a resource using a particular application, is received and determination is made as to whether the certificate is installed on the device based on an identification of the certificate by the application. If the certificate is installed on the device, then execution of the application and/or access to the resource is allowed. If the certificate is not installed on the device, then the request for execution and/or access is refused.

A mobile device can detect an idle state and, in response, initiate an access monitoring function to covertly monitor activity involving a human interaction with the mobile device. The covert monitoring is undetectable by a user of the mobile device. The mobile device can then detect a human interaction with the mobile device and, in response, cause the mobile device to covertly capture and log one or more human interactions with the mobile device. An authorized user of the mobile device is enabled to review the log of human interactions with the mobile device.

A mobile device can detect an idle state and, in response, initiate an access monitoring function to covertly monitor activity involving a human interaction with the mobile device. The covert monitoring is undetectable by a user of the mobile device. The mobile device can then detect a human interaction with the mobile device and, in response, cause the mobile device to covertly capture and log one or more human interactions with the mobile device. An authorized user of the mobile device is enabled to review the log of human interactions with the mobile device.

The present disclosure provides a data processing method for coping with ransomware, which encrypts data with a malicious intent and blocks an access to the data, to protect the data, and a program for executing the data processing method. In a computer apparatus that loads an application program stored in a memory onto a processor and carries out a predetermined processing according to the application program, on an operating system (OS) kernel which controls an access of the application program to hardware components of the computer apparatus, the processor reads the data stored in the memory, performs the predetermined processing at the request of the application program, determines whether a ransomware attack occurred for the data before storing the processed data back to the memory, and stores the processed data to the memory according to a determination result, thereby preventing the damage caused by the ransomware attack.

Input data for an operating system command of an automation process is received. The operating system command is generated based on the received input data. The generated operating system command is parsed to identify one or more metrics. The identified one or more metrics are automatically evaluated to determine a security risk associated with the generated operating system command.

Input data for an operating system command of an automation process is received. The operating system command is generated based on the received input data. The generated operating system command is parsed to identify one or more metrics. The identified one or more metrics are automatically evaluated to determine a security risk associated with the generated operating system command.

A method for safely starting an arithmetic logic unit that includes a plurality of computer kernels, software being started which includes multiple computer-program parts that form one or more computer programs. The method includes: implementation of an integrity check of the computer-program parts, the integrity check being carried out in parallelized fashion utilizing at least two of the plurality of computer kernels at least to some extent simultaneously; and execution of one computer program of the one or more computer programs by the arithmetic logic unit if the integrity of the computer-program parts, which form the computer program, was confirmed.

A method for deploying an information handling system (platform) determines whether a hardware key coupled to the platform constitutes a deployment key by validating a GUID of the key against a deployment key signature, generated by a trusted server and stored on the key. If the key is validated, a trust factor evaluation is performed by validating the deployment key against a second key, which is bound to a nonvolatile storage component containing a second key signature, generated by the trusted server based on a GUID of the nonvolatile storage component. Upon validating the trust factor, the platform boots into an unattended deployment mode loaded from the deployment key and validates an unattended deployment binary stored in the deployment key against the second key signature to establish a trusted execution session for loading unattended deployment modules from the deployment key and deploying the platform by executing the unattended deployment modules.

A method for deploying an information handling system (platform) determines whether a hardware key coupled to the platform constitutes a deployment key by validating a GUID of the key against a deployment key signature, generated by a trusted server and stored on the key. If the key is validated, a trust factor evaluation is performed by validating the deployment key against a second key, which is bound to a nonvolatile storage component containing a second key signature, generated by the trusted server based on a GUID of the nonvolatile storage component. Upon validating the trust factor, the platform boots into an unattended deployment mode loaded from the deployment key and validates an unattended deployment binary stored in the deployment key against the second key signature to establish a trusted execution session for loading unattended deployment modules from the deployment key and deploying the platform by executing the unattended deployment modules.

Provided herein are systems and methods for determining a likelihood that an executable comprises malware. A learning engine may determine a plurality of attributes of an executable identified in a computing environment, and a corresponding weight to assign to each of the plurality of attributes. Each of the plurality of attributes may be indicative of a level of risk for the computing environment. The learning engine may generate, according to the determined plurality of attributes and the corresponding weights, one or more scores indicative of a likelihood that the executable comprises malware. A rule engine may perform an action to manage operation of the executable, according to the generated one or more scores.