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71 lines
4.2 KiB
Markdown
71 lines
4.2 KiB
Markdown
# 8.20 DroidAnalytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware
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## What is your take-away message from this paper?
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The authors present DroidAnalytics, an Android malware analytic system for malware collection, signature generation, information retrieval, and malware association based on similarity score. Furthermore, DroidAnalytics can efficiently detect zero-day repackaged malware.
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## What are motivations for this work?
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An effective analytic system needs to address the following questions:
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- How to automatically collect and manage a high volume of mobile malware?
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- How to analyze a zero-day suspicious application, and compare or associate it with existingmalware families in the database?
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- How to perform information retrieval so to reveal similar malicious logic with existing malware, and to quickly identify the new malicious code segment?
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## What is the proposed solution?
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The system consists these modules:
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- Extensible Crawler: systematically build up the mobile applications database for malware analysis and association.
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- Dynamic Payload Detector: to deal with malware which dynamically downloads malicious codes via Internet or attachment files.
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- scans the package, identifies files using their magic numbers instead of file extension.
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- use the forward symbolic execution technique to trigger the download behavior.
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- Android App Information (AIS) Parser: it is used to represent *.apk* information.
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- Signature Generator: use a three-level signature generation scheme to identify each application, which is based on the mobile application, classes, methods. We generate a method's signature using the API call sequence, and given the signature of a method, create the signature of a class which composes of different methods, finally, the signature of an application is composed of all signatures of its classes.
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- Android API calls table: use the Java reflection to obtain all descriptions of the API calls.
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- Disassembling process: takes the Dalvik opcodes of the *.dex* file and transforms them to methods and classes.
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- Generate Lev3 signature: extracts the API call ID sequence as a string in each method, then hashes this string value to produce the method's signature.
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- Generate Lev2 signature: generate the Lev2 signature for each class based on the Lev3 signature of methods within that class.
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- Generate Lev1 signature: based on the Lev2 signatures.
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## What is the work's evaluation of the proposed solution?
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>We conduct three experiments and show how analysts can study malware, carry out similarity measurement between applications, as well as perform class association among 150,368 mobile applications in the database.
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- analyzing malware repackaging
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- analyzing malware which uses code obfuscation
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- analyzing malware with attachement files or dynamic payloads
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>we have used DroidAnalyt- ics to detect 2,494 malware samples from 102 families, with 342 zero-day malware samples from six different families.
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## What is your analysis of the identified problem, idea and evaluation?
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DroidAnalytics's signature generation is based on the following observation: For any functional application, it needs to invoke various Android API calls, and Android API calls sequence within a methods is difficult to modify.
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Traditional Hash vs Three-level Signature:
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- Traditional hash
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- Hackers can easily mutate a malware
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- Not flexible for analysis
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- Three-level signature
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- App, classes and methods
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- Defend against obfuscation
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- Facilitate analysis
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- Zero-day malware
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## What are the contributions?
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The authors present the design and implementation of DroidAnalytics:
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- DroidAnalytics automates the processes of malware collection, analysis and management.
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- DroidAnalytics uses a multi-level signature algorithm to extract the malware feature based on their semantic meaning at the opcode level.
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- DroidAnalytics associates malware and generates signatures at the app/class/method level.
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- Show how to use DroidAnalytics to detect zero-day repackaged malware.
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## What are future directions for this research?
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## What questions are you left with?
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