The term malware is a contraction of malicious software. Put simply, malware is any piece of software that was written with the intent of doing harm to data, devices or to people. Source: https://www.avg.com/en/signal/what-is-malware
In the past few years, the malware industry has grown very rapidly that, the syndicates invest heavily in technologies to evade traditional protection, forcing the anti-malware groups/communities to build more robust softwares to detect and terminate these attacks. The major part of protecting a computer system from a malware attack is to identify whether a given piece of file/software is a malware.
Microsoft has been very active in building anti-malware products over the years and it runs it’s anti-malware utilities over 150 million computers around the world. This generates tens of millions of daily data points to be analyzed as potential malware. In order to be effective in analyzing and classifying such large amounts of data, we need to be able to group them into groups and identify their respective families.
This dataset provided by Microsoft contains about 9 classes of malware. ,
Source: https://www.kaggle.com/c/malware-classification
Minimize multi-class error.
Multi-class probability estimates.
Malware detection should not take hours and block the user's computer. It should fininsh in a few seconds or a minute.
Source : https://www.kaggle.com/c/malware-classification/data For every malware, we have two files
.asm file (read more: https://www.reviversoft.com/file-extensions/asm)
.bytes file (the raw data contains the hexadecimal representation of the file's binary content, without the PE header)
Total train dataset consist of 200GB data out of which 50Gb of data is .bytes files and 150GB of data is .asm files: Lots of Data for a single-box/computer. There are total 10,868 .bytes files and 10,868 asm files total 21,736 files There are 9 types of malwares (9 classes) in our give data Types of Malware:
Ramnit
Lollipop
Kelihos_ver3
Vundo
Simda
Tracur
Kelihos_ver1
Obfuscator.ACY
Gatak
There are nine different classes of malware that we need to classify a given a data point => Multi class classification problem
Source: https://www.kaggle.com/c/malware-classification#evaluation
Metric(s):
Multi class log-loss
Confusion matrix
Objective: Predict the probability of each data-point belonging to each of the nine classes.
Constraints:
Class probabilities are needed.
Penalize the errors in class probabilites => Metric is Log-loss.
Some Latency constraints.
Split the dataset randomly into three parts train, cross validation and test with 64%,16%, 20% of data respectively
Train Test split
There are 10868 files of asm All the files make up about 150 GB The asm files contains :
- Address
- Segments
- Opcodes
- Registers
- function calls
- APIs With the help of parallel processing we extracted all the features.In parallel we can use all the cores that are present in our computer.
Here we extracted 52 features from all the asm files which are important.
We read the top solutions and handpicked the features from those papers/videos/blogs. Refer:https://www.kaggle.com/c/malware-classification/discussion
To extract the unigram features from the .asm files we need to process ~150GB of data Note: Below two cells will take lot of time (over 48 hours to complete) We will provide you the output file of these two cells, which you can directly use it
Adding 300 bytebigram,200 opcode bigram,200 opcode trigram,200 opcode tetragram ,first 200 image pixels Machine Learning Models on ASM Features + Byte Features + Advanced Features
MODEL | FEATURES | |
---|---|---|
random | Byte files | 2.45 |
knn | Byte files | 0.48 |
Logistic Regression | Byte files | 0.52 |
Random Forest Classifier | Byte files | 0.06 |
XgBoost Classification | Byte files | 0.07 |
knn | asmfiles | 0.21 |
Logistic Regression | asmfiles | 0.38 |
Random Forest Classifier | asmfiles | 0.03 |
XgBoost Classification | asmfiles | 0.04 |
Random Forest Classifier | Byte files+asmfiles | 0.04 |
XgBoost Classification | Byte files+asmfiles | 0.02 |
Logistic Regression | Byte files+asmfiles+advanced features | 1.12 |
XgBoost Classification | Byte files+asmfiles+advanced features | 0.01 |