Abstract: A system and method for providing stringent tamper resistant protection against changes to key system security features. The tamper protection is configured such that any changes to the policy can only occur from a configuration manager console, thereby preventing local device admin users or other malicious actors from altering the setting. Thus, tamper protection locks the selected service and prevents security settings from being changed through third-party apps and methods. When a system administrator enables the feature for an enterprise's workstations, only administrators will be able to change the service settings across a company's computers. The tamper protection policy is digitally signed in the backend before being deployed to endpoints, and the endpoint verifies the validity and intent of the policy, establishing that it is a signed package that only security operations personnel with the necessary administrator rights can control.

Abstract: A system and method for providing stringent tamper resistant protection against changes to key system security features. The tamper protection is configured such that any changes to the policy can only occur from a configuration manager console, thereby preventing local device admin users or other malicious actors from altering the setting. Thus, tamper protection locks the selected service and prevents security settings from being changed through third-party apps and methods. When a system administrator enables the feature for an enterprise's workstations, only administrators will be able to change the service settings across a company's computers. The tamper protection policy is digitally signed in the backend before being deployed to endpoints, and the endpoint verifies the validity and intent of the policy, establishing that it is a signed package that only security operations personnel with the necessary administrator rights can control.

Abstract: Technologies for detecting malware based on reinforcement learning model to detect whether a file is malicious or benign and to determine the best time to halt the file's execution in so detecting. The reinforcement learning model combined with an event classifier and a file classifier learns whether to halt execution after enough state information has been observed or to continue execution if more events are needed to make a highly confident determination. The algorithm disclosed allows the system to decide when to stop on a per file basis.

Abstract: Technologies for training systems for detecting malware based on reinforcement learning model. Such trained systems detect whether a file is malicious or benign and to determine the best time to halt the file's execution in so detecting. The reinforcement learning model combined with an event classifier and a file classifier learns whether to halt execution after enough state information has been observed or to continue execution if more events are needed to make a highly confident determination. The algorithm disclosed allows the system to decide when to stop on a per file basis.

Abstract: Technologies for detecting malware based on reinforcement learning model to detect whether a file is malicious or benign and to determine the best time to halt the file's execution in so detecting. The reinforcement learning model combined with an event classifier and a file classifier learns whether to halt execution after enough state information has been observed or to continue execution if more events are needed to make a highly confident determination. The algorithm disclosed allows the system to decide when to stop on a per file basis.

Abstract: Aspects of the subject matter described herein relate to malware detection using code analysis and behavior monitoring. In aspects, an anti-malware engine performs static analysis on program code and monitors behavior of the program code that is exhibited when the program code executes in a virtual and/or non-virtual environment. The anti-malware engine combines the results of both types of malware detection to determine whether the program code includes malware. The anti-malware engine may use feedback from one or more of the malware detection mechanism to direct additional malware detection (e.g., static and/or behavior detection) for the program code.