Adobe Malware Classifier – Perform quick, easy classification of binaries for malware analysis.
The tool was developed using models resultant from running the J48, J48 Graft, PART, and Ridor machine-learning algorithms on a dataset of approximately 100,000 malicious programs and 16,000 clean programs.
The tool extracts seven key features from an unknown binary, feeds them to one of the four classifiers or all of them, and presents its classification of the unknown binary
Features :
- malware classification
# Adobe(R) Malware Classifier
# Contributor: Karthik Raman, Adobe Systems Incorporated
# Dependencies: Python pefile, Python argparse
# Program to classify unknown Win32 binaries (EXEs or DLLs) into
# 0 = CLEAN
# 1 = DIRTY
# UNKNOWN
“”” Results on dataset of ~130000 dirty, ~ 16000 clean files:
(False Positives, True Negatives, True Positives, rates
J48 FP TN TP FN TP Rate FP Rate Accuracy
7683 37171 130302 3451 0.97419871 0.171289071 0.937662018
J48Graft FP TN TP FN TP Rate FP Rate Accuracy
6780 38074 129087 4666 0.965114801 0.151157087 0.935915166
PART FP TN TP FN TP Rate FP Rate Accuracy
7074 36492 125060 9412 0.930007734 0.162374329 0.907401791
Ridor FP TN TP FN TP Rate FP Rate Accuracy
7390 37935 114194 20930 0.845105237 0.163044677 0.843058149
“””
usage: AdobeMalwareClassifier.py [-h] [-f filename] [-n model] [-v [verbose]]
Classify an unknown binary as MALWARE or CLEAN.
optional arguments:
-h, –help show this help message and exit
-f filename The name of the input file
-n model The ordinal for model classifier: 0=all (default) | 1=J48 |
2=J48Graft | 3=PART | 4=Ridor
-v [verbose] Dump the PE data being processed
Download latest version : AdobeMalwareClassifier.py (31.0 kB)
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Read more in here : https://sourceforge.net/adobe/malclassifier