Dexofuzzy: Dalvik EXecutable Opcode Fuzzyhash

Dexofuzzy is a similarity digest hash for Android. It extracts Opcode Sequence from Dex file based on Ssdeep and generates hash that can be used for similarity comparison of Android App. Dexofuzzy created using Dex's opcode sequence can find similar apps by comparing hash.

Requirements

Dexofuzzy requires the following modules:

ssdeep 3.3 or later

Install

Install on CentOS 6.10, 7.6, 8.0

$ yum install epel-release
$ yum install libffi-devel ssdeep ssdeep-devel python3-pip python3-devel libtool
$ pip3 install dexofuzzy

Install on Debian 8.11, 9.9, 10.0

$ apt-get install libffi-dev libfuzzy-dev python3-pip
$ pip3 install dexofuzzy

Install on Linux Mint 3, 18.3, 19.1

$ apt-get install libffi-dev libfuzzy-dev python3-pip python3-dev
$ pip3 install setuptools wheel
$ pip3 install dexofuzzy

Install on Ubuntu 14.04 LTS, 16.04 LTS, 18.04 LTS

$ apt-get install libffi-dev libfuzzy-dev
$ pip3 install dexofuzzy

Install on Windows 7, 10

The ssdeep DLL binaries for Windows are included in ./dexofuzzy/bin/ directory.
included in (https://github.com/intezer/ssdeep-windows)
included in (https://github.com/MacDue/ssdeep-windows-32_64)

$ pip3 install dexofuzzy

Usage

usage: dexofuzzy [-h] [-f SAMPLE_FILENAME] [-d SAMPLE_DIRECTORY] [-m] [-g N]
                 [-s DEXOFUZZY DEXOFUZZY] [-c CSV_FILENAME] [-j JSON_FILENAME]
                 [-l]

Dexofuzzy - Dalvik EXecutable Opcode Fuzzyhash v0.0.5

optional arguments:
   -h, --help                     show this help message and exit
   -f SAMPLE_FILENAME, --file SAMPLE_FILENAME
                                  the sample to extract dexofuzzy
   -d SAMPLE_DIRECTORY, --directory SAMPLE_DIRECTORY
                                  the directory of samples to extract dexofuzzy
   -m, --method-fuzzy             extract the fuzzyhash based on method of the sample
                                  (default use the -f or -d option)
   -g N, --clustering N           N-gram cluster the dexofuzzy of the sample
                                  (default use the -d option)
   -s DEXOFUZZY DEXOFUZZY, --score DEXOFUZZY DEXOFUZZY
                                  score the dexofuzzy of the sample
   -c CSV_FILENAME, --csv CSV_FILENAME
                                  output as CSV format
   -j JSON_FILENAME, --json JSON_FILENAME
                                  output as json format
                                  (include method fuzzy or clustering)
   -l, --error-log                output the error log

Output Format Example

FileName, FileSha256, FileSize, OpcodeHash, Dexofuzzy

$ dexofuzzy -f SAMPLE_FILE
sample.apk,80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835,42959,94d36ca47485ca4b1d05f136fa4d9473bb2ed3f21b9621e4adce47acbc999c5d,48:U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY:UHMHZ4/zeGD2+Cap3y9Q
Running Time : 0.016620635986328125

Method Fuzzy

$ dexofuzzy -f SAMPLE_FILE -m
80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835,80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835,42959,d89c3b2c2620b77b1c0df7ef66ecde6d70f30b8a3ca15c21ded4b1ce1e319d38,48:U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY:UHMHZ4/zeGD2+Cap3y9Q
[
    "3:mWc0R2gLkcT2AVA:mWc51cTnVA",
    "3:b0RdGMVAn:MA",
    "3:y+6sMlHdNy+BGZn:y+6sMh5En",
    "3:y4CdNy/GZn:y4C+En",
    "3:dcpqn:WEn",
    "3:EN:EN",
    ...
]

Clustering

$ dexofuzzy -d SAMPLE_DIRECTORY -g 7
80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835,80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835,42959,d89c3b2c2620b77b1c0df7ef66ecde6d70f30b8a3ca15c21ded4b1ce1e319d38,48:U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY:UHMHZ4/zeGD2+Cap3y9Q
ffe8c426c3a8ade648666bb45f194c1e84fb499b126932997c4d50cdfc4cc8f3,ffe8c426c3a8ade648666bb45f194c1e84fb499b126932997c4d50cdfc4cc8f3,46504,4a7039eefb7a8c292bcbd3e9fa232f4e6b136eedb9a114eb32aa360742b3f28f,48:B2KmUCNc2FuGgy9fbdD7uPrEMc0HZj0/zeGn5:B2+Cap3y9pDHMHZ4/zeG5
[
    {
        "file_name": "80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835",
        "file_sha256": "80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835",
        "file_size": "42959",
        "opcode_hash": "d89c3b2c2620b77b1c0df7ef66ecde6d70f30b8a3ca15c21ded4b1ce1e319d38",
        "dexofuzzy": "48:U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY:UHMHZ4/zeGD2+Cap3y9Q",
        "clustering": [
            {
                "file_name": "80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835",
                "file_sha256": "80cd7786fa42a257dcaddb44823a97ff5610614d345e5f52af64da0ec3e62835",
                "file_size": "42959",
                "opcode_hash": "d89c3b2c2620b77b1c0df7ef66ecde6d70f30b8a3ca15c21ded4b1ce1e319d38",
                "dexofuzzy": "U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY",
                "signature": "U7uPrEM"
            },
            {
                "file_name": "ffe8c426c3a8ade648666bb45f194c1e84fb499b126932997c4d50cdfc4cc8f3",
                "file_sha256": "ffe8c426c3a8ade648666bb45f194c1e84fb499b126932997c4d50cdfc4cc8f3",
                "file_size": "46504",
                "opcode_hash": "4a7039eefb7a8c292bcbd3e9fa232f4e6b136eedb9a114eb32aa360742b3f28f",
                "dexofuzzy": "B2KmUCNc2FuGgy9fbdD7uPrEMc0HZj0/zeGn5",
                "signature": "7uPrEMc"
            }
        ]
    },
    {
        ...
    }
]

Python API

To compute a Dexofuzzy of dex file, use hash function:

hash(dex_binary_data)

>>> import dexofuzzy
>>> with open('classes.dex', 'rb') as dex:
...     dex_data = dex.read()
>>> dexofuzzy.hash(dex_data)
'48:U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY:UHMHZ4/zeGD2+Cap3y9Q'

hash_from_file(apk_file or dex_file)

>>> import dexofuzzy
>>> dexofuzzy.hash_from_file('sample.apk')
'48:U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY:UHMHZ4/zeGD2+Cap3y9Q'
>>> dexofuzzy.hash_from_file('classes.dex')
'48:U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY:UHMHZ4/zeGD2+Cap3y9Q'

The compare function returns the match between 2 hashes, an integer value from 0 (no match) to 100.

compare(dexofuzzy_1, dexofuzzy_2)

>>> import dexofuzzy
>>> with open('classes.dex', 'rb') as dex:
...     dex_data = dex.read()
>>> hash1 = dexofuzzy.hash(dex_data)
>>> hash1
'48:U7uPrEMc0HZj0/zeGnD2KmUCNc2FuGgy9fY:UHMHZ4/zeGD2+Cap3y9Q'
>>> hash2 = dexofuzzy.hash_from_file('classes2.dex')
>>> hash2
'48:B2KmUCNc2FuGgy9fbdD7uPrEMc0HZj0/zeGn5:B2+Cap3y9pDHMHZ4/zeG5'
>>> dexofuzzy.compare(hash1, hash2)
50

Tested on

CentOS 6.10, 7.7, 8.0
Debian 8.11, 9.9, 10.0
Linux Mint 3, 18.3, 19.1
Ubuntu 14.04 LTS, 16.04 LTS, 18.04 LTS
Windows 7, 10

Publication

Shinho Lee, Wookhyun Jung, Sangwon Kim, Jihyun Lee, Jun-Seob Kim, Dexofuzzy: Android Malware Similarity Clustering Method using Opcode Sequence. Virus Bulletin, November 2019.

License

This project is licensed under the GNU General Public License v2 or later (GPLv2+). Please see LICENSE located at the project's root for more details.

hanul93/Dexofuzzy