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DroidScribe: Classifying Android Malware Based on Runtime Behavior

Research output: Chapter in Book/Report/Conference proceedingConference paper

Santanu Kumar Dash, Guillermo Suarez-Tangil, Salahuddin Khan, Kimberly Tam, Mansour Ahmadi, Johannes Kinder, Lorenzo Cavallaro

Original languageEnglish
Title of host publicationMobile Security Technologies (MoST)
DOIs
Publication statusPublished - 26 May 2016

Bibliographical note

Acceptance Rate: 28%

Documents

  • DroidScribe Classifying Android Malware_DASH_Accepted8March2016_GREEN AAM

    2016most.pdf, 571 KB, application/pdf

    31/05/2018

    Accepted author manuscript

    © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted.

King's Authors

Abstract

The Android ecosystem has witnessed a surge in malware, which not only puts mobile devices at risk but also increases the burden on malware analysts assessing and categorizing threats. In this paper, we show how to use machine learning to automatically classify Android malware samples into families with high accuracy, while observing only their runtime behavior. We focus exclusively on dynamic analysis of runtime behavior to provide a clean point of comparison that is dual to static approaches. Specific challenges in the use of dynamic analysis on Android are the limited information gained from tracking low-level events and the imperfect coverage when testing apps, e.g., due to inactive command and control servers. We observe that on Android, pure system calls do not carry enough semantic content for classification and instead rely on lightweight virtual machine introspection to also reconstruct Android-level inter-process communication. To address the sparsity of data resulting from low coverage, we introduce a novel classification method that fuses Support Vector Machines with Conformal Prediction to generate high-accuracy prediction sets where the information is insufficient to pinpoint a single family.

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