Determining model protection level on-device based on malware detection in similar devices

Abstract

Methods, systems and devices for identifying, classifying, modeling, and responding to mobile device behaviors may include using lightweight processes to monitor and analyze various conditions and device behaviors to detect an instance of a non-benign behavior, increasing a level of security or scrutiny to identify other instances of non-benign behavior, and notifying select computing devices of the increased security risk so that they may also increase their security / scrutiny levels. For example, a computing device may be configured to perform a first type of analysis operations (e.g., lightweight analysis operations) to determine whether there is an increased security risk, and perform a second type of analysis operations (e.g., robust analysis operations) in response to determining that there is an increased security risk to determine whether there are additional security risks that are different from the security risk detected via the performance of the first type of analysis operations.

Description

Background technique [0001] Cellular and wireless communication technologies have grown tremendously in recent years. Better communication hardware, larger networks, and more reliable protocols have fueled this growth. Wireless service providers are now able to offer their customers an ever-expanding set of features and services, and provide users with unprecedented levels of access to information, resources, and communications. To keep pace with these service improvements, mobile electronic devices (eg, cellular phones, tablet computers, laptop computers, etc.) have become ever more powerful and complex. This complexity creates new opportunities for malware, software conflicts, hardware failures, and other similar errors or phenomena to adversely affect the long-term and ongoing performance and power utilization levels of the mobile device. Accordingly, it would be beneficial to consumers to identify and correct conditions and / or mobile device behavior that may adversely af...

AI Technical Summary

Problems solved by technology

To keep pace with these service improvements, mobile electronic devices (e.g., cellular phones, tablet computers, laptop computers, etc.) have become ever more powerful and complex

This complexity creates new opportunities for malware, software conflicts, hardware failures, and other similar errors or phenomena to adversely affect the long-term and sustained performance and power utilization levels of mobile devices

Accordingly, it would be beneficial to consumers to identify and correct conditions and / or mobile device behavior that may adversely affect the long-term and sustained performance and power utilization levels of a mobile device

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