Inhaltszusammenfassung

The number of Android devices in both home and professional environments is growing rapidly and, as time passes, so does the number of aging devices. Outdated devices are often less secure, e.g., due to a lack of available patches. Thus, environments in which such devices are deployed tend to possess a broader attack surface. Therefore, in this work we investigate approaches for the detection of aging devices. We set up an environment using multiple aged Android devices (smartphones and a tab...The number of Android devices in both home and professional environments is growing rapidly and, as time passes, so does the number of aging devices. Outdated devices are often less secure, e.g., due to a lack of available patches. Thus, environments in which such devices are deployed tend to possess a broader attack surface. Therefore, in this work we investigate approaches for the detection of aging devices. We set up an environment using multiple aged Android devices (smartphones and a tablet) running different Android releases. Our paper focuses on the recognition of these device using a classifier with several features extracted from passive traffic recordings. Depending on the Android version, we were able to classify the devices with an F-Measure of 97.9 to 99.0% and an accuracy of 99.0 to 99.5% under laboratory conditions, and an accuracy of 94.4 to 98.9% when including surrounding traffic.» weiterlesen» einklappen

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DFG Fachgebiet:
Informatik

DDC Sachgruppe:
Informatik