Intel Corporation

Aravind Subramanian

Abhay Kanhere

Ravi L Sahita

Xiaoning Li

Mingwei Zhang

Yi Gai

Chih-Yuan Yang

Paul Henry Hohensee

Haitao Feng

A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.

Malicious object detection in a runtime environment

Technologies for native code invocation using binary analysis are described. A computing device for invoking native code from managed code using binary analysis receives a call from a thread executing a managed code segment to execute a native code segment. The computing device performs a binary analysis of the native code segment and generates, from the binary analysis, a complexity indicator that indicates a level of complexity of the native code segment by comparing the native code segment to at least one predefined complexity rule. Additionally, the computing device stores a status of the thread based on the complexity indicator and executes the native code segment. Other embodiments are described and claimed.

Technologies for native code invocation using binary analysis

Growing community of inventors

San Jose, CA, United States of America

Aravind Subramanian