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Attack of the Clones: Detecting Cloned Applications on Android Markets

Attack of the Clones: Detecting Cloned Applications on Android Markets. Jonathan Crussell 1,2 , Clint Gibler 1 , and Hao Chen 1 1 University of California, Davis 2 Sandia National Labs Source: ESORICS 2012. Outline. Introduction Background Threat Model

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Attack of the Clones: Detecting Cloned Applications on Android Markets

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  1. Attack of the Clones: Detecting Cloned Applications on Android Markets Jonathan Crussell1,2, Clint Gibler1, and Hao Chen1 1 University of California, Davis 2 Sandia National Labs Source: ESORICS 2012

  2. Outline • Introduction • Background • Threat Model • Clone Detection Approaches and Related Work • Methodology • Evaluation • Case Studies • Discussion • Conclusion

  3. Introduction • Much of the user experience of Android relies on third-party apps. • Android has numerous marketplaces. • Protect users from malicious apps. • Protect developers from plagiarists.

  4. Introduction • Developers can charge directly for their apps. • Offer free apps that are ad-supported or contain in-game billing. • Some apps have two version. • Paid app －＞ cracked &release for free • Free app －＞ cloned & change ad libraries

  5. Introduction

  6. Background • Android Markets • Android Application Structure

  7. Threat Model－Definition of “Clone”. • Clones occur when two applications have similar code but have different ownership. • IgnoreThird-party librariesMultiple versions of the same application if they have the same ownership.

  8. Resistance to Evasion Techniques. • High level modifications • Method Restructurings • Control Flow Alterations • Addition/Deletion • Reordering

  9. Non Goals • Find cloning in native code. • Determine which applications are the victims and which are clones.

  10. Clone Detection Approaches－Feature Based • Feature based approaches analyze a program and extract a set of features. • Number or size of classes, methods, loops, or variables to included libraries. • Low detection rate or high false positive rate.

  11. Clone Detection Approaches－Structure Based • Structure based systems convert programs into a stream of tokens and then compare the streams between two programs. • More robustly than feature based systems. • JPLAG, Winnowing and MOSS. • Comparing DEX byte code streams could be a quite quick and scalable method to find exactly or near exactly copied code. • But byte code streams contain no higher level semantic knowledge about the code.

  12. Clone Detection Approaches－PDG Based • Program Dependence Graph: each node is a statement each edge shows a dependency between statements two types of dependencies: data and control • A data dependency edge between statements and exists if there is a variable in whose value depends on . • A control dependency between two statements exists if the truth value of the first statement controls whether the second statement executes.

  13. Related Work • Androguard, DEXCD and DroidMOSS. • All these approaches are structure based or structure based approximations. • None of these tools use any semantic information to aid in detecting plagiarism.

  14. Methodology

  15. Selecting Potentially Cloned Applications • The goal of an application plagiarist is to entice unwary users to choose her cloned application instead of the original. • Name and description.

  16. Determining Application Similarity Based on Attributes • We use Solrto mimic the search engines on Android markets. • Attributes of the apps: name, package, market, owner, and description

  17. Constructing PDGs • dex2jar: Convert both apps’ code from the DEX format to a JAR. • WALA: Construct PDGs for each method in every class of the applications. • Only data dependency edges: More robust against statement reordering, insertion and deletion.

  18. Comparing PDGs-Excluding Common Libraries • Ad library Admob, Facebook API, etc. • Dumped both the package name and SHA-1 hash of known library files and recorded the most frequent SHA-1 hashes for each library.

  19. Lossless and Lossy Filters • Lossless filter: Removes PDGs from consideration that are smaller than a specified size (< 10 nodes). • Lossy filter: Calculate a frequency vector for each of the methods in the pair. • This vector counts how many times a specific node type occurs in the PDG. • Compare these two vectors using hypothesis testing (G-test).

  20. Subgraph Isomorphism • Find a mapping between nodes in and nodes in . • Subgraphisomorphism is NPComplete. • VF2 algorithm.

  21. Computing Similarity Scores • For each method (excluding the methods in known libraries) in application , let be the number of nodes in this method’s PDG. Find the best match of this PDG in ’s PDGs and denote it as . • Similarity score:

  22. Evaluation • 75,000 free apps from 13 Android markets. • Randomly selected 9,400 pairs from the potential clones. • Hadoop: parallelize DNADroid. • HDFS: share data across a small cluster. • The average throughput of DNADroid on this small cluster is 0.71 application pairs per minute.

  23. Similarity between Applications

  24. Similarity between Applications

  25. Clustering Cloned Applications

  26. Clustering Cloned Applications

  27. Filter Performance

  28. Filter Performance

  29. Visual and Behavioral Verification

  30. Case Studies

  31. “Benign” Cloning • DNADroid found 30 pairs that both have a 100% similarity score. • Translation.

  32. Changes to Advertising Libraries • We can see when an application has most likely been cloned for monetary gain. • Ex: XWind Downloader • For the 141 apps, we found that 91 (65%) of these pairs had different libraries, all of which included changes to advertising libraries.

  33. Malware Added to an Application • “HippoSMS” is a malicious application requires 10 permissions. • It shares the same package name as a Chinese video player requires 11 permissions. • 6 permissions that video playerdoesn’t use.

  34. Two Variants of the Same Malware • Two malicious apps that are identified by VirusTotalas being variants of the “BaseBridge” malware family. • Both applications have been stripped of meaningful class and method names. • DNADroid found coverages of 35% and 28% between the two variants.

  35. Use of Freeware Cracking Tool in the Wild • AntiLVLDecompiling an app with baksmaliInserts a new file:SmaliHook.classAnd hide AntiLVL’s modifications from the app itself by returning the original file size, MD5, and signatures. • Android License Verification Library (LVL), Amazon Appstore DRM and Verizon DRM. • 189 of 310 applications containing SmaliHook.class • 235 of 310 containing references to AntiLVL in their signature files. • Only 8% of our total apps were acquired from Chinese markets, 88% of the apps including AntiLVL traces were from Chinese markets.

  36. Discussion

  37. False Positive • Since it is a serious allegation to claim an application is a clone, we design DNADroid to have a very low false positive rate.

  38. False Negative • Cloned applications often have similar attributes as the original. (?) • There exist advanced program transformations that can evade PDG-based clone detection.

  39. Comparison to Other Approaches • Androguard: miss 18% • DEXCDhad problems running on the pairs DNADroid identified. • DroidMOSS is not currently publicly available.

  40. Performance • DNADroid are more expensive but result in fewer false positives and false negatives.

  41. Conclusion • DNADroid is a tool for finding clones on a large scale. • We evaluated DNADroidon applications crawled from 13 Android markets. Identified at least 141 apps that have been cloned An additional 310 apps that were cracked with AntiLVL • We describe five case studies • DNADroid has a very low false positive rate • DNADroid is an effective tool.

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