![]() Intents Actions - "", "_UPDATE", "_COMPLETED", "_SHORTCUT", "", "_PACKAGE_REPLACED", "_PRESENT", "" Receivers/Providers - "com.fb.iwidget.ExpandWidgetProvider", "com.fb.iwidget.ActionReceiver", Services - "", "com.fb.iwidget.MainService", "com.fb.iwidget.SnapAccessService" Table 2 presents the examples of static features extracted from captured dataset.Īctivities - "com.fb.iwidget.OverlayActivity", "", "", "com.fb.iwidget.MainActivity", "com.fb.iwidget.PreferencesActivity", "com.fb.iwidget.PickerActivity", "com.fb.iwidget.IntroActivity" System features (such as camera and internet) The permissions requested by application: It protects the privacy of the user and is needed to access sensitive user data (such as contacts and SMS) Metadata: It is basically an additional option to store information that can be accessed through the entire project Activities: An android activity is one screen of the android app's user interface HTTP API is provided to allow the full download of the unaltered APKs from the Androzoo dataset.ĪndroidManifest.xml contains a lot of features that can be used for static analysis. ![]() A weekly updated list containing all the detailed information about the apps is created. The architecture is developed to collect the Androzoo dataset from different sources including official android market, Google Play, Anshi, AppChina, 1mobile, and Genome project dataset. Family - Number of captured samplesįor benign android apps, we used the Androzoo dataset, which currently contains more than eight million unique android apps and the number is still growing. The families of each malware category in Table 1 along with the numbers of the captured samples are as presented below: Table 1 presents the details of 14 android malware categories along with number of respective families and samples in the dataset.Ĭategory - Number of families - Number of samples We searched for similar malware samples to categorize malware samples in dataset with similar characteristics. We used VirusTotal to specify malware family and label the dataset by following a consensus of 70% anti-viruses to incorporate reliability in labeled dataset. The dataset includes 200K benign and 200K malware samples totalling to 400K android apps with 14 prominent malware categories and 191 eminent malware families.ĬCCS supported us to capture the real-world android malware apps for analysis. ![]() This research work proposes a new comprehensive and huge android malware dataset, named CCCS-CIC-AndMal-2020. There are many techniques available to identify and classify android malware based on machine learning, but recently, deep learning has emerged as a prominent classification method for such samples. It is an open challenge for cybersecurity experts. Detecting android malware in smartphones is an essential target for cyber community to get rid of menacing malware samples.Īndroid malware is one of the most serious threats on the internet which has witnessed an unprecedented upsurge in recent years. ![]() Android malware industry is becoming increasingly disruptive with almost 12,000 new android malware instances every day. Malicious domains are one of the major threat of android malware is the root cause of various security problems on the internet. ![]()
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