AVClass malware labeling tool
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README.md
avclass_alias_detect.py
avclass_generic_detect.py
avclass_labeler.py

README.md

AVClass

AVClass is a malware labeling tool.

You give it as input the AV labels for a large number of malware samples (e.g., VirusTotal JSON reports) and it outputs the most likely family name for each sample that it can extract from the AV labels. It can also output a ranking of all alternative names it found for each sample.

The design and evaluation of AVClass is detailed in our RAID 2016 paper:

Marcos Sebastián, Richard Rivera, Platon Kotzias, and Juan Caballero. AVClass: A tool for Massive Malware Labeling. In Proceedings of the International Symposium on Research in Attacks, Intrusions and Defenses, September 2016.

In a nutshell, AVClass comprises two phases: preparation (optional) and labeling. Code for both is included, but most users will be only interested in the labeling, which outputs the family name for the samples. The preparation produces a list of aliases and generic tokens used by the labeling. If you use our default aliases and generic tokens lists, you do not need to run the preparation.

Why is AVClass useful?

Because a lot of times security researchers want to extract family information from AV labels, but this process is not as simple as it looks, especially if you need to do it for large numbers (e.g., millions) of samples. Some advantages of AVClass are:

  1. Automatic. AVClass removes manual analysis limitations on the size of the input dataset.

  2. Vendor-agnostic. AVclass operates on the labels of any available set of AV engines, which can vary from sample to sample.

  3. Cross-platform. AVclass can be used for any platforms supported by AV engines, e.g., Windows or Android malware.

  4. Does not require executables. AV labels can be obtained from online services like VirusTotal using a sample's hash, even when the executable is not available.

  5. Quantified accuracy. We have evaluated AVClass on 5 publicly available malware datasets with ground truth. Details are in the above RAID 2016 paper.

  6. Open source. The code is available and we are happy to incorporate suggestions and improvements so that the security community benefits from AVClass.

Limitations

The main limitation of AVClass is that its output depends on the input AV labels. It tries to compensate for the noise on those labels, but cannot identify the family of a sample if AV engines do not provide non-generic family names to that sample. In particular, it cannot label samples if at least 2 AV engines do not agree on a non-generic family name. Results on 8 million samples showed that AVClass could label 81% of the samples. In other words, it could not label 19% of the samples because their labels contained only generic tokens.

Still, there are many samples that AVClass can label and thus we believe you will find it a useful tool. We recommend you to read the discussion section in our RAID 2016 paper for more details.

Labeling

The labeler takes as input a JSON file with the AV labels of malware samples (-vt or -lb switches), a file with generic tokens (-gen switch), and a file with aliases (-alias switch). It outputs the most likely family name for each sample. If you do not provide alias or generic tokens files, the default ones in the data folder are used.

$./avclass_labeler.py -lb data/malheurReference_lb.json -v > malheurReference.labels

The above command labels the samples whose AV labels are in the data/malheurReference_lb.json file. It prints the results to stdout, which we redirect to the malheurReference.labels file. The output looks like this:

aca2d12934935b070df8f50e06a20539 adrotator
67d15459e1f85898851148511c86d88d adultbrowser

which means sample aca2d12934935b070df8f50e06a20539 is most likely from the adrotator family and 67d15459e1f85898851148511c86d88d from the adultbrowser family.

The verbose (-v) switch makes it output an extra malheurReference_lb.verbose file with all families extracted for each sample ranked by the number of AV engines that use that family. The file looks like this:

aca2d12934935b070df8f50e06a20539  [(u'adrotator', 8), (u'zlob', 2)]
ee90a64fcfaa54a314a7b5bfe9b57357  [(u'swizzor', 19)]
f465a2c1b852373c72a1ccd161fbe94c  SINGLETON:f465a2c1b852373c72a1ccd161fbe94c

which means that for sample aca2d12934935b070df8f50e06a20539 there are 8 AV engines assigning adrotator as the family and
another 2 assigning zlob. Thus, adrotator is the most likely family. On the other hand, for ee90a64fcfaa54a314a7b5bfe9b57357 there are 19 AV engines assigning swizzor as family, and no other family was found. The last line means that for sample f465a2c1b852373c72a1ccd161fbe94c no family name was found in the AV labels. Thus, the sample is placed by himself in a singleton cluster with the name of the cluster being the sample's hash.

Note that the sum of the number of AV engines may not equal the number of AV engines with a label for that sample in the input file because the labels of some AV engines may only include generic tokens that are removed by AVClass.

Input JSON format

AVClass supports two input JSON formats:

  1. VirusTotal JSON reports (-vt file), where each line in file should be the full JSON of a VirusTotal report as fetched through the VirusTotal API.

  2. Simplified JSON (-lb file), where each line in file should be a JSON with (at least) these fields: {md5, sha1, sha256, scan_date, av_labels}. There is an example of such input file in data/malheurReference_lb.json

Why have 2 different input formats?

We believe most users will get the AV labels using VirusTotal. However, AVClass is IO-bound and a VirusTotal report in addition to the AV labels and hashes includes much other data that AVClass does not need. Thus, when applying AVClass to millions of samples, reducing the input file size by removing unnnecessary data significantly improves efficiency. Furthermore, users could obtain AV labels from other sources and the simpler the input JSON format, the easier to convert those AV labels into an input file.

Multiple input files

AVClass can handle multiple input files putting the results in the same output files (if you want results in separate files, process each input file separately).

It is possible to provide the -vt and -lb input options multiple times.

$./avclass_labeler.py -vt <file1> -vt <file2> > all.labels
$./avclass_labeler.py -lb <file1> -lb <file2> > all.labels

There are also -vtdir and -lbdir options that can be used to provide an input directory where all files are VT (-vtdir) or simplified (-lbdir) JSON reports

$./avclass_labeler.py -vtdir <directory> > all.labels

It is also possible to combine -vt with -vtdir and -lb with -lbdir, but you cannot combine input files of different format. Thus, this command works:

$./avclass_labeler.py -vt <file> -vtdir <directory> > all.labels

But, this one throws an error:

$./avclass_labeler.py -vt <file1> -lb <file2> > all.labels

At this point you have read the most important information on how to use AVClass. The following sections describe steps that most users will not need.

Labeling: Family Ranking

AVClass has a -fam switch to output a file with a ranking of the families assigned to the input samples.

$./avclass_labeler.py -lb data/malheurReference_lb.json -v -fam > malheurReference.labels

will produce a file called malheurReference_lb.families with two columns:

virut 441
allaple 301
podnuha 300

indicating that 441 samples were classified in the virut family, 301 as allaple, and 300 as podnuha.

This switch is very similar to using the following shell command:

$cut -f 2 malheurReference.labels | sort | uniq -c | sort -nr

The main difference is that using the -fam switch all SINGLETON samples, i.e., those for which no label was found, are grouped into a fake SINGLETONS family, while the shell command would leave each singleton as a separate family.

Labeling: PUP Classification

AVClass also has a -pup switch to classify a sample as Potentially Unwanted Program (PUP) or malware. This classification looks for PUP-related keywords (e.g., pup, pua, unwanted, adware) in the AV labels and was proposed in our CCS 2015 paper:

Platon Kotzias, Srdjan Matic, Richard Rivera, and Juan Caballero. Certified PUP: Abuse in Authenticode Code Signing. In Proceedings of the 22nd ACM Conference on Computer and Communication Security, Denver, CO, October 2015

$./avclass_labeler.py -lb data/malheurReference_lb.json -v -pup > malheurReference.labels

With the -pup switch the output of the malheurReference.labels file looks like this:

aca2d12934935b070df8f50e06a20539 adrotator 1
67d15459e1f85898851148511c86d88d adultbrowser 0

The digit at the end is a Boolean flag that indicates sample aca2d12934935b070df8f50e06a20539 is (likely) PUP, but sample 67d15459e1f85898851148511c86d88d is (likely) not.

In our experience the PUP classification is conservative, i.e., if it says the sample is PUP, it most likely is. But, if it says that it is not PUP, it could still be PUP if the AV labels do not contain PUP-related keywords. Note that it is possible that some samples from a family get the PUP flag while other samples from the same family do not because the PUP-related keywords may not appear in the labels of all samples from the same family. To address this issue, you can combine the -pup switch with the -fam switch. This combination will add into the families file the classification of the family as malware or PUP, based on a majority vote among the samples in a family.

$./avclass_labeler.py -lb data/malheurReference_lb.json -v -pup -fam > malheurReference.labels

will produce a file called malheurReference_lb.families with five columns:

# Family  Total Malware PUP FamType
virut 441 441 0 malware
magiccasino 173 0 173 pup
ejik  168 124 44  malware

For virut, the numbers indicate all the 441 virut samples are classified as malware, and thus the last column states that virut is a malware family. For magiccasino, all 173 samples are labeled as PUP, thus the family is PUP. For ejik, out of the 168 samples, 124 are labeled as malware and 44 as PUP, so the family is classified as malware.

Labeling: Ground Truth Evaluation

If you have ground truth for some malware samples, i.e., you know the true family for those samples, you can evaluate the accuracy of the labeling output by AVClass on those samples with respect to that ground truth. The evaluation metrics used are precision, recall, and F1 measure. See our RAID 2016 paper above for their definition.

$./avclass_labeler.py -lb data/malheurReference_lb.json -v -gt data/malheurReference_gt.tsv -eval > data/malheurReference.labels

The output includes these lines:

Calculating precision and recall
3131 out of 3131
Precision: 90.81  Recall: 93.95 F1-Measure: 92.35

The last line corresponds to the accuracy metrics obtained by comparing AVClass results with the provided ground truth.

Each line in the data/malheurReference_gt.tsv file has two tab-separated columns:

0058780b175c3ce5e244f595951f611b8a24bee2 CASINO

which indicates that sample 0058780b175c3ce5e244f595951f611b8a24bee2 is known to be of the CASINO family. Each sample in the input file should also appear in the ground truth file. Note that the particular label assigned to each family does not matter. What matters is that all samples in the same family are assigned the same family name (i.e., the same string in the second column)

The ground truth can be obtained from publicly available malware datasets. The one in data/malheurReference_gt.tsv comes from the Malheur dataset. There are other public datasets with ground truth such as Drebin or Malicia.

Preparation: Generic Token Detection

The labeling takes as input a file with generic tokens that should be ignored in the AV labels, e.g., trojan, virus, generic, linux. By default, the labeling uses the data/default.generics generic tokens file. You can edit that file to add additional generic tokens you feel we are missing.

In our RAID 2016 paper we describe an automatic approach to identify generic tokens, which requires ground truth, i.e., it requires knowing the true family for each input sample. Not only that, but the ground truth should be large, i.e., contain at least one hundred thousand samples. In our work we identified generic tokens using as ground truth the concatenation of all datasets for which we had ground truth. This requirement of a large ground truth dataset is why we expect most users will skip this step and simply use our provided default file.

If you want to test generic token detection you can do:

 $./avclass_generic_detect.py -lb data/malheurReference_lb.json -gt data/malheurReference_gt.tsv -tgen 10 > malheurReference.gen 

Each line in the data/malheurReference_gt.tsv file has two tab-separated columns:

0058780b175c3ce5e244f595951f611b8a24bee2 CASINO

which indicates that sample 0058780b175c3ce5e244f595951f611b8a24bee2 is known to be of the CASINO family.

The -tgen 10 switch is a threshold for the minimum number of families where a token has to be observed to be considered generic. If the switch is ommitted, the default threshold of 8 is used.

The above command outputs two files: malheurReference.gen and malheurReference_lb.gen. Each of them has 2 columns: token and number of families where the token was observed. File malheurReference.gen is the final output with the detected generic tokens for which the number of families is above the given threshold. The file malheurReference_lb.gen has this information for all tokens. Thus, malheurReference.gen is a subset of malheurReference_lb.gen.

However, note that in the above command you are trying to identify generic tokens from a small dataset since Drebin only contains 3K labeled samples. Thus, malheurReference.gen only contains 25 identified generic tokens. Using those 25 generic tokens will produce significantly worse results than using the generic tokens in data/default.generics. For more details you can refer to our RAID 2016 paper.

Preparation: Alias Detection

Different vendors may assign different names (i.e., aliases) for the same family. For example, some vendors may use zeus and others zbot as aliases for the same malware family. The labeling takes as input a file with aliases that should be merged. By default, the labeling uses the data/default.aliases aliases file. You can edit that file to add additional aliases you feel we are missing.

In our RAID 2016 paper we describe an automatic approach to identify aliases. Our alias detection approach requires as input the AV labels for large set of samples, e.g., several million samples. In contrast with the generic token detection, the input samples for alias detection do not need to be labeled, i.e., no need to know their family. In our work we identified aliases using as input the largest of our unlabeled datasets, which contained nearly 8M samples. This requirement of a large input dataset is why we expect most users will skip this step and simply use our provided default file.

If you want to test alias detection you can do:

$./avclass_alias_detect.py -lb data/malheurReference_lb.json -nalias 100 -talias 0.98 > malheurReference.aliases

The -nalias threshold provides the minimum number of samples two tokens need to be observed in to be considered aliases. If the switch is not provided the default is 20.

The -talias threshold provides the minimum fraction of times that the samples appear together. If the switch is not provided the default is 0.94 (94%).

The above command outputs two files: malheurReference.aliases and malheurReference_lb.alias. Each of them has 6 columns:

  1. t1: token that is an alias
  2. t2: family for which t1 is an alias
  3. |t1|: number of input samples where t1 was observed
  4. |t2|: number of input samples where t2 was observed
  5. |t1^t2|: number of input samples where both t1 and t2 were observed
  6. |t1^t2|/|t1|: ratio of input samples where both t1 and t2 were observed over the number of input samples where t1 was observed.

File malheurReference.aliases is the final output with the detected aliases that satisfy the -nalias and -talias thresholds. The file malheurReference_lb.alias has this information for all tokens. Thus, malheurReference.aliases is a subset of malheurReference_lb.alias.

However, note that in the above command you are trying to identify aliases from a small dataset since Drebin only contains 3K samples. Thus, malheurReference.aliases only contains 6 identified aliases. Using those 6 aliases will produce significantly worse results than using the aliases in data/default.aliases. As mentioned, to improve the identified aliases you should provide as input several million samples. For more details you can refer to our RAID 2016 paper.

Support

If you need help with AVClass, want to discuss the project, or want to contribute generic tokens and/or aliases, you can join the AVClass mailing list.

Contributors

Several members of the MaliciaLab at the IMDEA Software Institute have contributed code to AVClass including: Marcos Sebastián, Richard Rivera, Platon Kotzias, Srdjan Matic, and Juan Caballero.