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Lightweight data-flow tracker for realtime behavioral analysis using control flow
Lightweight data-flow tracker for realtime behavioral analysis using control flow
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机译:轻量级数据流跟踪器,用于使用控制流进行实时行为分析
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摘要
Methods and devices for detecting performance-degrading behaviors include identifying a data source component that inputs data into an application executing on a mobile device, and identifying a data sink component that consumes data output from the application. Using a measured runtime control-flow parameter, a likelihood that the data source component is a critical data resource may be determined. Using the probability value, a behavior model that identifies a mobile device feature associated with the critical data resource may be updated and used to determine whether the software application is malicious. Measured runtime control-flow parameters may include a program execution distance between data source and sink components based on heuristics. Determining program execution distances between data sources and sinks may include computing call graph distances by comparing a source call stack length and a sink call stack length, or by counting method invocations or functional calls between data sources and sinks.
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