python毕业设计课题-基于异常检测技术的恶意代码对抗样本识别+源代码+文档说明

# 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](https://software.imdea.org/~juanca/papers/avclass_raid16.pdf): > 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. ## Labeling The labeler takes as input a JSON file with the AV labels of malware samples (-vt or -lb options), a file with generic tokens (-gen option), and a file with aliases (-alias option). 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. ```shell $./avclass_labeler.py -lb ../examples/malheurReference_lb.json -v > malheurReference.labels ``` The above command labels the samples whose AV labels are in the *../examples/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) option 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 three input JSON formats: 1. VirusTotal v2 API JSON reports (*-vt file*), where each line in the input *file* should be the full JSON of a VirusTotal v2 API response to the */file/report* endpoint, e.g., obtained by querying https://www.virustotal.com/vtapi/v2/file/report?apikey={apikey}&resource={hash} There is an example VirusTotal v2 input file in examples/vtv2_sample.json 2. VirusTotal v3 API JSON reports (*-vt file -vt3*), where each line in the input *file* should be the full JSON of a VirusTotal API version 3 response with a *File* object report, e.g., obtained by querying https://www.virustotal.com/api/v3/files/{hash} There is an example VirusTotal v3 input file in examples/vtv3_sample.json 3. Simplified JSON (*-lb file*), where each line in *file* should be a JSON with (at least) these fields: {md5, sha1, sha256, av_labels}. There is an example of such input file in *examples/malheurReference_lb.json* **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. ```shell $./avclass_labeler.py -vt <file1> -vt <file2> ``` ```shell $./avclass_labeler.py -lb <file1> -lb <file2> ``` 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: ```shell $./avclass_labeler.py -vtdir <directory> ``` 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: ```shell $./avclass_labeler.py -vt <file> -vtdir <directory> ``` But, this one throws an error: ```shell $./avclass_labeler.py -vt <file1> -lb <file2> ``` ## Labeling: Family Ranking AVClass has a -fam option to output a file with a ranking of the families assigned to the input samples. ```shell $./avclass_labeler.py -lb ../examples/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 option is very similar to using the following shell command: ```shell $cut -f 2 malheurReference.labels | sort | uniq -c | sort -nr ``` The main difference is that using the -fam option 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 option 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](https://software.imdea.org/~juanca/papers/malsign_ccs15.pdf): > 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 ```shell $./avclass_labeler.py -lb ../examples/malheurReference_lb.json -v -pup > malheurReference.labels ``` With the -pup option 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 option with the -fam option. 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. ```shell $./avclass_labeler.py -lb ../examples/malheurReference_lb.json -v -pup -fam > malheurReference.labels ``` will produce a file called *malheurReference_lb.families* with five columns: ``` # Family Total Malware PUP FamTyp