Experiments in malware detection and classification using machine learning techniques.
1. Microsoft Malware Classification Challenge
https://www.kaggle.com/c/malware-classification
1.1 Feature Engineering
Initial feature engineering consisted of extracting various keyword counts from the ASM files
as well as the entropy and file size from the BYTE files of the 10868 malware samples in the training set.
Image files of the first 1000 bytes of the ASM and BYTE files were created and combined with
keyword and entropy data. This resulted in a set of 2018 features.
Flow control graphs and call graphs were generated for each ASM sample. A feature set was
then generated from the graphs, including graph maximum delta, density, diameter and function
counts etc.
1.2 Feature Selection
Statistical analysis of the feature set using chi-squared tests to remove features that are
independent of the class labels or have low variance. The BYTE file images were found to be weak
learners and were removed from the feature set. A comparison of the best features from the chi-squared
tests with reduced feature sets of between 10% - 50% of the original features.
1.2.1 Selection Comparison
Testing with an ExtraTreesClassifier and 10-fold cross validation produced the following results:
- Original ASM Keyword Counts (1006 features): logloss = 0.034
- 10% Best ASM Features with Entropy and Image Features (202 features): logloss = 0.0174
- 20% Best ASM with Entropy and Image Features (402 features): logloss = 0.0164
- 30% Best ASM with Entropy and Image Features plus Feature Statistics (623 features):
multiclass logloss = 0.0133
accuracy score = 0.9978
Confusion Matrix:
[[1540 0 0 0 0 1 0 0 0]
[ 1 2475 2 0 0 0 0 0 0]
[ 0 0 2942 0 0 0 0 0 0]
[ 1 0 0 474 0 0 0 0 0]
[ 2 0 0 0 38 2 0 0 0]
[ 3 0 0 0 0 748 0 0 0]
[ 1 0 0 0 0 0 397 0 0]
[ 0 0 0 0 0 0 0 1225 3]
[ 0 0 0 0 0 0 0 8 1005]]
- 40% Best ASM and image features with feature statistics:
ExtraTreesClassifier with 1000 estimators on 10868 training samples and 823 features
using 10-fold cross validation:
multiclass logloss = 0.0135
accuracy score = 0.9976
Confustion Matrix:
[[1541 0 0 0 0 0 0 0 0]
[ 1 2475 2 0 0 0 0 0 0]
[ 0 0 2942 0 0 0 0 0 0]
[ 1 0 0 474 0 0 0 0 0]
[ 5 0 0 0 37 0 0 0 0]
[ 5 0 0 0 0 746 0 0 0]
[ 1 0 0 0 0 0 397 0 0]
[ 0 0 0 0 0 0 0 1227 1]
[ 0 0 0 0 0 0 0 9 1004]]
1.2.2 Feature Selection Summary
The performance of the ExtraTreesClassifier is optimal at around 30% of ASM and image features
with highest variance plus sample statistics, entropy and file size. Adding call graph features
produced a marginal improvement. It is possible that better classification accuracy would be
achieved by using an ensemble of different classifiers with the ASM, image and call graph
feature sets as separate inputs to the various classifiers.
1. Shannon's Entropy by malware class. A score of 0.0 means the bytes are all the same value,
a score of 1.0 means every byte in the file has a different value.
2. Shannon's Entropy by file size. A score of 0.0 means the bytes are all the same value,
a score of 1.0 means every byte in the file has a different value.
3. Assembler register EDX by ESI counts.
1.5 Conclusions
The best accuracy scores were achieved with XGBoost (99.81%) and ExtraTreesClassifier (99.76%) using a
feature set of 623 ASM, image and entropy features. Marginal improvements could be achieved using additional
features and ensemble methods, however due to the limited sample size further efforts are unlikely to produce
significant improvements in prediction accuracy. Analysis will now focus on much larger sample sizes from
VirusShare.com as described in the following sections.
VirusShare.com regularly publishes huge collections of malware binaries for use by researchers.
Each malware archive is currently around 25GB in size. Several of the latest archives have been
downloaded to use as training and test sets. The archives used are:
- Training set: VirusShare_00251.zip and VirusShare_00252.zip (131072 malware samples)
VirusShare_00263.zip and VirusShare_00264.zip (131072 malware samples)
VirusShare_APT1_293.zip (293 malware samples)
- Testing set:
2.1 Automated Unpacking and Disassembly of Malware Binaries
Using Cuckoo Sandbox and unpack.py for behaviourial analysis, unpacking the binaries and
dumping process memory, for intransigent samples, manual unpacking with Immunity Debugger and IDA Pro.
Tools:
- Cuckoo Sandbox (https://github.com/cuckoosandbox/cuckoo)
- unpack.py (https://malwaremusings.com/2013/02/26/automated-unpacking-a-behaviour-based-approach/)
(https://github.com/malwaremusings/unpacker)
- IDA Pro 5.0 (https://www.hex-rays.com/products/ida/support/download_freeware.shtml)
- Immunity Debugger (https://www.immunityinc.com/products/debugger/)
- Volatility (https://github.com/volatilityfoundation)
- Ildasm.exe (https://msdn.microsoft.com/en-us/library/f7dy01k1(v=vs.110).aspx)
- ndisasm (http://www.nasm.us/pub/nasm/releasebuilds/2.12.02/)
- TrID (http://mark0.net/soft-trid-e.html)
- ClamAV (clamav.net)
- Windows Defender
- MalwareBytes Anti-Malware
- VirusTotal.com
ClamAV and Windows Defender used for initial training label generation or VirusTotal.com aggregate classification
if they cannot identify the culprit. MalwareBytes was also used but it crashed at the end of the scan
and the log files could not be recovered.
AV Scan Results:
Results: VirusShare_00251
- 57529 files classified as malicious.
- 8007 files classified as non-malicious.
Results: VirusShare_00252
- 56625 files classified as malicious.
- 8911 files classified as non-malicious.
Results: VirusShare_00263
- 51612 files classified as malicious.
- 13924 files classified as non-malicious.
Results: VirusShare_00264
- 42274 files classified as malicious.
- 23262 files classified as non-malicious.
Results: VirusShare_APT1_293
- 292 files classified as malicious.
- 1 file classified as non-malicious.
Total Malware Types: 8334
Total Malware Families: 2737
Total Files: 262437
2.2.1 Graphs
4. Top 10 Malware Counts.
5. Top 10 Compiler/Packer Counts.
6. VirusShare 251 Call Graph - Vertex by Edge Count.
IDA Pro and objdump for disassembly of binaries to .asm text files.
Feature sets will consist of:
- Entropy and file size from packed binaries.
- Entropy and file size from unpacked binaries.
- File magic signatures and TrID signatures.
- ASM features from disassembled unpacked binaries.
- Executable header features.
- Call Graph Features.
- Function counts extracted from call graphs.
- Sample Statistics.
- Behavioural features from Cuckoo Sandbox reports.
- Memory features from Volatility reports.
2.4 Feature Selection and Reduction
1. PE/COFF Binaries: (Chi2 Tests)
VS251 Feature Sets: 54911 samples.
240 PE ASM and Header Features.
?? PE ASM Function Count Features.
VS252 Feature Sets: 46165 samples.
271 PE ASM and Header Features.
?? PE ASM Function Count Features.
VS263 Feature Sets: 40974 samples.
203 PE ASM and Header Features.
?? PE ASM Function Count Features.
VS264 Feature Sets: 14366 samples.
243 PE ASM and Header Features.
?? PE ASM Function Count Features.
2. ELF Binaries:
3. Java Bytecode:
4. Javascript:
5. HTML:
6. PDF:
2.5 Model Selection
2.5.1 PE/COFF Model Selection
Model selection with 10-fold cross validation:
1. ExtraTreesClassifier: VS251 100 estimators accuracy score = 0.912
500 estimators accuracy score = ?.??
1000 estimators accuracy score = memory fail
VS252 100 estimators accuracy score = 0.888 (12.75 minutes)
500 estimators accuracy score = ?.??
1000 estimators accuracy score = ?.??
VS263 100 estimators accuracy score = 0.903 (9.63 minutes)
500 estimators accuracy score = ?.???
1000 estimators accuracy score = ?.??
VS264 100 estimators accuracy score = 0.889 (2.27 minutes)
500 estimators accuracy score = 0.890 (14.57 minutes)
1000 estimators accuracy score = ?.??
2. XGBoost: VS251 100 estimators accuracy score = ?.??
XGBoost: VS252 100 estimators accuracy score = ?.??
XGBoost: VS263 100 estimators accuracy score = ?.??
XGBoost: VS264 100 estimators accuracy score = ?.??
3. LightGBM: VS251 100 estimators accuracy score = 0.892
VS252 100 estimators accuracy score = 0.676 (171.23 minutes)
VS263 100 estimators accuracy score = ?.??
VS264 100 estimators accuracy score = 0.758 (9.26 minutes)
200 estimators accuracy score = 0.750 (18.53 minutes)
4. RandomForestClassifier: VS251 100 estimators accuracy score = 0.903
500 estimators accuracy score = ?.??
1000 estimators accuracy score = ?.??
VS252 100 estimators accuracy score = 0.881 (81.34 minutes)
VS263 100 estimators accuracy score = ?.??
VS264 100 estimators accuracy score = 0.879 (15.45 minutes)
Model Stacks/Ensembles:
1. One input layer of classifiers -> 1 output layer classifier.
Layer 1: Six x layer one classifiers: (ExtraTrees x 2, RandomForest x 2, XGBoost x 1, LightGBM x 1)
Layer 2: One classifier: (ExtraTrees) -> final labels
2. Voting (Democratic and weighted).
Democratic: Six x layer one classifiers: (ExtraTrees x 2/RandomForest x 2/XGBoost/LightGBM)
-> (democratic vote, geometric and sum means) -> final labels
Weighted: Six x layer one classifiers: (ExtraTrees x 2/RandomForest x 2/XGBoost/LightGBM)
-> (weighted vote: ExtraTrees double weight, geometric and sum means) -> final labels
3. Multiple layers of classifiers.
Layer one -> layer two -> layer 3 -> final labels:
Layer 1: ExtraTrees x 2, RandomForest x 2, XGBoost x 1, LightGBM x 1
Layer 2: ExtraTrees x 2, RandomForest x 2, XGBoost x 1, LightGBM x 1
Layer 3: ExtraTrees x 1
4. Combined PE/COFF features + function count features:
Layer 1 -> layer 2 -> final labels
Layer 1 (A MODELS): Combined features layer one (ExtraTrees x 2, RandomForest x 2, XGBoost x 1, LightGBM x 1)
Layer 1 (B MODELS): Function count features layer one (ExtraTrees x 2, RandomForest x 2, XGBoost x 1, LightGBM x 1)
Layer 2: ExtraTrees x 1 -> final labels
5. Combine outputs from 1, 2, 3 and 4 -> vote -> final labels
2.5.2 ELF Model Selection
2.5.3 Java Bytecode Model Selection
2.5.4 Javascript Model Selection
TODO:
2.6 Conclusions
TODO:
2.7 Workflows
2.7.1 Training Label Generation
1. Antivirus scans using ClamAV and Windows Defender.
> clamscan -v -r /directory/containing/the/nastiness > clamav-report.txt
> Windows Defender (See notes in section 7 on extracting windows defender logs).
2. Generate scalar training labels for each malware type and family.
> process_av_reports.py
> combine_av_reports.py
> generate_train_labels.py
If installing from source, after building and installing you have
problems loading other packages it is because of the xgboost-0.4-py2.7.egg.pth
file that the install script dumps in the python dist-packages
directory. You will have to delete the .pth file then
go change the installation of the xgboost egg and egg-info files in the
python dist-packages directory from:
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