Applying machine learning classifiers to dynamic android malware detection at scale
2013 9th international wireless communications and mobile …, 2013•ieeexplore.ieee.org
The widespread adoption and contextually sensitive nature of smartphone devices has
increased concerns over smartphone malware. Machine learning classifiers are a current
method for detecting malicious applications on smartphone systems. This paper presents
the evaluation of a number of existing classifiers, using a dataset containing thousands of
real (ie not synthetic) applications. We also present our STREAM framework, which was
developed to enable rapid large-scale validation of mobile malware machine learning …
increased concerns over smartphone malware. Machine learning classifiers are a current
method for detecting malicious applications on smartphone systems. This paper presents
the evaluation of a number of existing classifiers, using a dataset containing thousands of
real (ie not synthetic) applications. We also present our STREAM framework, which was
developed to enable rapid large-scale validation of mobile malware machine learning …
The widespread adoption and contextually sensitive nature of smartphone devices has increased concerns over smartphone malware. Machine learning classifiers are a current method for detecting malicious applications on smartphone systems. This paper presents the evaluation of a number of existing classifiers, using a dataset containing thousands of real (i.e. not synthetic) applications. We also present our STREAM framework, which was developed to enable rapid large-scale validation of mobile malware machine learning classifiers.
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