An Obfuscation-Neglect Android Malware Scoring System
Obfuscation-Neglect Android Malware
Android malware analysis engine is not a new story. Every antivirus company has their own secrets to build it. With curiosity, we develop a malware scoring system from the perspective of Taiwan Criminal Law in an easy but solid way.
We have an order theory of criminal which explains stages of committing a crime. For example, crime of murder consists of five stages, they are determined, conspiracy, preparation, start and practice. The latter the stage the more we’re sure that the crime is practiced.
According to the above principle,
we developed our order theory of android malware. We develop five stages to see if the malicious activity is being practiced. They are 1. Permission requested. 2. Native API call. 3. Certain combination of native API. 4. Calling sequence of native API. 5. APIs that handle the same register. We not only define malicious activities and their stages but also develop weights and thresholds for calculating the threat level of a malware.
Malware evolved with new techniques to gain difficulties for reverse engineering. Obfuscation is one of the most commonly used techniques. In this talk, we present a Dalvik bytecode loader with the order theory of android malware to neglect certain cases of obfuscation.
Our Dalvik bytecode loader consists of functionalities such as 1. Finding cross reference and calling sequence of the native API. 2. Tracing the bytecode register. The combination of these functionalities (yes, the order theory) not only can neglect obfuscation but also match perfectly to the design of our malware scoring system.
Easy to Use and Reading Friendly Report
Quark is very easy to use and also provides flexible output formats. There are 3 types of output report: detail report, call graph, and summary report. Please see below for more details.
This is a how we examine a real android malware (candy corn) with one single rule (crime).
$ quark -a sample/14d9f1a92dd984d6040cc41ed06e273e.apk \ -r rules/ \ --detail
and the report will look like:
Call Graph for Every Potential Malicious Activity
You can add the
-g option to the quark command, and you can
get the call graph (only those rules match with 100% confidence)
quark -a Ahmyth.apk -r quark-rules/ -s -g
You can add the
-c option to the quark command, and you can
output the rules classification with mutual parent function (only those rules match with 100% confidence)
quark -a Ahmyth.apk -r quark-rules/ -s -c
Examine with rules.
quark -a sample/14d9f1a92dd984d6040cc41ed06e273e.apk \ -r rules/ \ --summary
$ git clone https://github.com/quark-engine/quark-engine.git; cd quark-engine/quark $ pipenv install --skip-lock $ pipenv shell
Make sure your python version is
3.8, or you could change it from
Pipfile to what you have.
--help to see the detailed usage description.
$ quark --help
Test It Out
You may refer to the Quark Engine Document for more details of testing and development information.
Analysis Reports of Real Malware
Quark Engine will soon provide analysis reports of real malware! For your best experience of viewing the report, please use desktop web browser. We're planning to make a mobile version of report. If you really want to see the very first version of report please visit here
Also, we will soon give out our new detection rules!
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size quark_engine-20.11-py3-none-any.whl (35.8 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size quark-engine-20.11.tar.gz (20.9 kB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for quark_engine-20.11-py3-none-any.whl