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To perform classification in a scalable and automated fashion, the approach we have developed dynamically obtains labeled training examples using the Google Image Search API based on user defined queries (for example, a query for retrieving examples of anti-virus icons could be anti-virus desktop icon). Using the resulting labeled image data, we have trained and compared a number of image classifiers. To evaluate these classifiers we hand-labeled malware images with their correct class and computed confusion matrices for more than a dozen classes of malware images (for example, "fake anti-virus", "fake web browser", etc.), revealing that our classification techniques varied in accuracy, with some image category detectors (such as "fake word processor") providing deployable levels of accuracy and others generating misclassifications at an unacceptable rate. In conclusion, by presenting what we believe to be compelling early results vis-a-vis both malware image set similarity and malware image classification, we hope to inspire the malware research community to both adopt image analysis in practice and further research into this understudied research area Read more↗

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Updated1 year ago
Checked8 months ago
Keywordsus park this is deeperent tracking app behaviors with nothing changed phone for evasiveaandroid malware
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