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.
Dexofuzzy requires the following modules:
- ssdeep 3.3 or later
$ yum install epel-release
$ yum install libffi-devel ssdeep ssdeep-devel python3-pip python3-devel libtool
$ pip3 install dexofuzzy$ apt-get install libffi-dev libfuzzy-dev python3-pip
$ pip3 install dexofuzzy$ apt-get install libffi-dev libfuzzy-dev python3-pip python3-dev
$ pip3 install setuptools wheel
$ pip3 install dexofuzzy$ apt-get install libffi-dev libfuzzy-dev
$ pip3 install dexofuzzy- 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 dexofuzzyusage: 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- 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"
}
]
},
{
...
}
] 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- 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
- Shinho Lee, Wookhyun Jung, Sangwon Kim, Jihyun Lee, Jun-Seob Kim, Dexofuzzy: Android Malware Similarity Clustering Method using Opcode Sequence. Virus Bulletin, November 2019.
Copyright (C) 2019 ESTsecurity.
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.