Attack of the Clones: Detecting Cloned Applications

on Android MarketsJonathan Crussell1,2, Clint Gibler1, and Hao Chen1

1 University of California, Davis2 Sandia National LabsSource: ESORICS 2012

Outline

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

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.

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

Introduction

Background

• Android Markets• Android Application Structure

Threat Model － Definition of “Clone”.• Clones occur when two applications

have similar code but have different ownership.• Ignore

Third-party libraries Multiple versions of the same application if they have the same ownership.

Resistance to Evasion Techniques.

• High level modifications• Method Restructurings• Control Flow Alterations• Addition/Deletion• Reordering

Non Goals

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

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.

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.

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.

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.

Methodology

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.

Determining Application Similarity Based on Attributes

• We use Solr to mimic the search engines on Android markets.• Attributes of the apps:

name, package, market, owner, and description

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.

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.

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).

Subgraph Isomorphism

• Find a mapping between nodes in and nodes in .• Subgraph isomorphism is NPComplete.• VF2 algorithm.

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:

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.

Similarity between Applications

Similarity between Applications

Clustering Cloned Applications

Clustering Cloned Applications

Filter Performance

Filter Performance

Visual and Behavioral Verification

Case Studies

“Benign” Cloning

• DNADroid found 30 pairs that both have a 100% similarity score.• Translation.

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.

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 player doesn’t use.

Two Variants of the Same Malware

• Two malicious apps that are identified by VirusTotal as 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.

Use of Freeware Cracking Tool in the Wild• AntiLVL

Decompiling an app with baksmali Inserts a new file: SmaliHook.class And 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.

Discussion

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.

False Negative

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

based clone detection.

Comparison to Other Approaches

• Androguard: miss 18%• DEXCD had problems running on the pairs DNADroid identified.• DroidMOSS is not currently publicly available.

Performance

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

Conclusion

• DNADroid is a tool for finding clones on a large scale.• We evaluated DNADroid on 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.