Jan 30, 2024, 07:06 PM
Detection and analysis by human and machine
This groundbreaking guide to Android malware summarizes years of research by machine learning experts in academia and members of the Meta and Google Android security teams into a comprehensive introduction to detecting common threats facing the Android ecosystem today.
Explore the history of Android malware since the OS was first released and then practice static and dynamic approaches to analyzing real malware samples. Next, examine the machine learning techniques that can be used to detect malicious apps, the types of classification models that defenders can implement to achieve these detections, and the various malware features that can be used as input for these models. Adapt these machine learning strategies to the identification of malware categories such as banking Trojans, ransomware and SMS fraud.
https://files.catbox.moe/weuqgt.pdf
This groundbreaking guide to Android malware summarizes years of research by machine learning experts in academia and members of the Meta and Google Android security teams into a comprehensive introduction to detecting common threats facing the Android ecosystem today.
Explore the history of Android malware since the OS was first released and then practice static and dynamic approaches to analyzing real malware samples. Next, examine the machine learning techniques that can be used to detect malicious apps, the types of classification models that defenders can implement to achieve these detections, and the various malware features that can be used as input for these models. Adapt these machine learning strategies to the identification of malware categories such as banking Trojans, ransomware and SMS fraud.
https://files.catbox.moe/weuqgt.pdf
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