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APISeqFewShot

This is the code repository for publishsed paper "A Novel Few-Shot Malware Classification Approach for Unknown Family Recognition with Multi-Prototype Modeling" submit to Computer & Security. All the model implementation details (including proposed SIMPLE model) and baselines are included and all the models are implemented with Python 3.7+ and PyTorch 1.4 (cuda in default).

(Note: Turn to MalFusionFSL for a more elegant and structured implementation, which implements few-shot malware classification of static, dynamic and fusion of both. Configurating data_source and fusion | type as 'sequence' in config/train.json can singly run dynamic analysis classification that is equal to running this code repository. Contact [email protected] for any questions.).

Cite our published paper on Computer & Security:

   @article{WANG2021102273,
      title = {A Novel Few-Shot Malware Classification Approach for Unknown Family Recognition with Multi-Prototype Modeling},
      journal = {Computers & Security},
      pages = {102273},
      year = {2021},
      issn = {0167-4048},
      doi = {https://doi.org/10.1016/j.cose.2021.102273},
      url = {https://www.sciencedirect.com/science/article/pii/S0167404821000973},
      author = {Peng Wang and Zhijie Tang and Junfeng Wang}
   }

Declaration

This code repository has always been used for experiments of our research, thus it is designed for engineering purpose, where some code logic is hard to explained clearly in short words. We DO NOT PROMISE it work properly on other people's machines. We publicate this repository to faciliate the few-shot malware classification research and if you use our code in your work, please refer to our paper. If there are any problems, please raise them in 'issues'.

Acknowledgement

We implement our SIMPLE model based on the code of Allen et.al: IMP, and we really appreciate their kind open-source paper project.

How to Run this Code

Dataset

Download our dataset on Google Drive: Few-shot-Dynamic-Analysis if you do not want to get confused by the data preprocessing process. To access raw data of this dataset, visit VirusShare website or APIMDS dataset release website to download the datasets described in the paper. A detailed data preprocessing description is located in the later section of this README.

Running Requirement

To run this code repo, you must have a CUDA-enabled GPU installed on your machine. Following is our running environment:

  • OS: Manjaro Linux KDE 20.2
  • CPU: Intel Core i9-10900X 3.7GHz, 20 cores
  • GPU: 4-channel RTX 2080 Ti, 11 GB
  • Memory: 128 GB
  • Cuda: 10.2

Hyper-Parameter Setting

Design Philosophy

To ensure reproducibility, hyper-parameters are configured by JSON form config file, located in run/runConfig.json. During model initialization, models will automatically read the hyper-parameters in this JSON configuration file and modify their structures accordingly. This enables a more convenient and readable way to configure model parameters, compared to running arguments.

Running Task

Everytime you start to train a model, a new running task will be created and a doc directory containing the running configuration and results will be made in doc folder with the name of version parameter. So remember to change version parameter in run/runConfig.json when you intend to train a new model as to prevent from unexpected overwriting. Other important parameters are introduced in the following sections. Trained torch model parameter state dictionaries are saved in models folder.

Environment Path Setting

Another point worth to mention is that, before running, you should add an item to Ns key-value object in runConfig.json, where key equals your dataset name and value equals how many items are there in each family. Also, please add an object to 'platform-node' item, where key equals the host name, value equals the base path your datasets are located in. (host name can be obtained by calling platform.node()). This makes sure model can correctly find your dataset and load the data from datasets.

Train

Make sure your dataset folder has been created properly (unzip our processed dataset or follow our preprocessing instruction) and placed in the path configured by platform/(your node name)/datasetBasePath, then just run run/run.py:

cd APISeqFewShot/run
python run.py

It will load the data files and run config to configure the running settings. If you want to visualize the training process, set useVisdom in runConfig.json to true, and turn on the visdom server by

python -m visdom.server

Then visit localhost:8097.

Training statistics will display in console like:

----------------Test in Epoch 99--------------------
***********************************
train acc:  0.642
train loss:  5.168325721025467
----------------------------------
val acc: 0.5968000000000001
val loss: 2.0870852395892143
----------------------------------
best val loss: 2.0870852395892143
best epoch: 99
time consuming:  19.70339059829712
time remains:  01:38:11
***********************************

Test

It is very similar to training models but you need to change testConfig.json to configure test settings. Note that if you want to run testing on train or validate subset, set testSubdataset to train or validate, but it is test in default.

Make sure your dataset folder has been created properly (unzip our processed dataset or follow our preprocessing instruction) and placed in the path configured by platform/(your node name)/datasetBasePath, then just run run/test.py:

cd APISeqFewShot/run
python test.py

Testing statistics will display in console like:

200 Epoch
--------------------------------------------------
Acc: 0.926400
Loss: 0.231254
Current Avg Acc: 0.922600
Time: 3.38
time remains:  00:00:27
--------------------------------------------------

Data Preprocessing (Optional)

This section provides detailed data preprocessing process on raw datasets. Almost all the preprocessing-related code are located in preliminaries package. We assume all the sequence data files are in 'JSON' form, where the api sequence is a list with key 'apis'. We DO NOT RECOMMEND use our code to do preprocessing work because we divide the process into many subprocesses and dump log for each subprocess, instead of making them a end-to-end pipeline. This brings in confusion when using our code when preprocessing, thus we recommend to write your own code to do preprocessing, including several main steps:

  1. Run malware binary samples in Cuckoo sandbox to get api sequence in JSON form

  2. Drop sequence files where sequence has length less than 10, according to our paper

  3. Remove redundancy in api sequence (successive invocation of the same api twice or more), which can also be done by calling preliminaries/preprocessing.py/removeApiRedundance (set 'class_dir' to 'False' assumes that each JSON file are located in an independent directory, with the same name of JSON file). This function will overwrite the original JSON file, so please remember to backup.

  4. Extract N-gram (N=3 in our paper) of api sequence and replace the api sequence item with api N-gram item

  5. Calculate TF-IDF value of each N-gram item (you may refer to our code extractors/TFIDF.py). Leave only top-k (k=2000 in our paper) TF-IDF value N-gram item in sequence and repeat Step.2

  6. Use some scan tools (like VirusTotal) to make analysis report for each malware sample, then extract a sole label for each malware sample by some tools (like AVClass). Collect samples with the same family tag

  7. Sample a fixed number (20 in our paper) of sequence files from each family to a directory, whose name is the same as the family

  8. Run our code preliminaries/dataset.py/makeDatasetDirStruct to make dataset directory structure, and move all the family folders to 'all' directory

  9. Train GloVe embedding on all the family folders and output a mapping file and a embedding matrix, you may refer to our code uitls/GloVe.py (it requires for Python2 env and glove-python package). Move the index mapping file (rename to wordMap.json) and word embedding matrix (rename to matrix.npy, NumPy.ndarray type) to 'data' folder.

  10. Split the families in 'all' dataset to 'train', 'validate' and 'test', according to our paper (move family folders)

  11. Run our code run/make_data_files.py to generate torch data files to be loaded (change the parameters before running)

After these steps, the whole dataset directory structure look like this:

(Your dataset name)
    |-- all
        |-- (all family folders...)
    |-- train
        |-- (train family folders...)
    |-- validate
        |-- (validate family folders...)
    |-- test
        |-- (test family folders...)
    |-- doc    
    |-- models
    |-- data
        |-- train
            |-- data.npy
            |-- idxMapping.json
            |-- seqLength.json
        |-- validate
            |-- data.npy
            |-- idxMapping.json
            |-- seqLength.json
        |-- test
            |-- data.npy
            |-- idxMapping.json
            |-- seqLength.json
        |-- wordMap.json
        |-- matrix.npy

Project Organization (Optional)

Code files are organized by function and locate in different folders. To dive deeper to our project, you can check the following detailed description of our project. Otherwise, you can just ignore this section and only focus on parameters tuning and experiments.

run

This folder mainly contains the launching script for training and testing experiment and run-time parameter controlling related files.

  • config.py Most relates to the running configuration saving and version controlling.

  • run.py Actual launching script for training experiments. This file contains training config reading, manager initialization, dataset parsing, model running/validating, line plotting and result reporting. It will read the runConfig.py file to load the training parameters.

  • test.py Actual launching script for testing experiments. This file is highly similar to run.py but only focus on testing, so some parts are removed.

  • finetuning.py Actual launching script for finetuning experiments. It reads the ftConfig.py to load the fine-tuning parameters and uses SGD optimizer to fine-tune an untrained model(SIMPLE) for several iterations. Then the fine-tuned model is runned for testing to produce the final results. Note that we use a linear layer to generate the classification results after sequence embedding.

  • runConfig.json Configuration file for training experiments. Most important items include: model, shot(k), way(n), dataset, maximum sequence length, optimizer and learning rate. Note that the base paths of datasets are required to state in this file, as "platform-node" item. On different hosts, dataset bases can be altered by modifying this item.

  • testConfig.json Configuration file for testing experiments. This file is highly similar to runConfig.json, except for: testing version and model name, testing iteration and testing subdataset(train, validate or test).

  • starter.py Actual launching script for some trivial dataset manipulation programs. Typical manipulations include: data file collection and generating(sequence length setting), dataset splitting, dataset preprocessing and etc. In short, this file calls the functions in other modules to complete the target.

preliminaries

This folder mainly contains the modular scripts for dataset preprocessing, dataset formalization, malware scanning and malware labeling.

  • avclass.py It most contains the auxiliary scripts for supporting avclass tool.

  • dataset.py It contains the implementations for making and splitting dataset. Original data is located at "(dataset)/train", "(dataset)/validate" and "(dataset)/test". Then these distributed data are collected and wrapped in a single matrix file and sequence lengths are also recorded in another json file, which are all located in "(dataset)/data/train/", "(dataset)/data/validate/" and "(dataset)/data/test/".

  • embedding.py Aggregates the malware sequences in the dataset and trains the W2V embedding model.

  • preprocessing.py It contains the implementations for: API alias mapping, sequence redundancy removing, API frequency statistics, API repeated subsequence removing and class-wise API data file collecting and etc.

  • virustotal.py Most relates to some malware scanning operations using the service provided by VirusTotal website.

utils

It contains the utility modules to facilitate the dataset generating, training/testing experiments, path managing, statistics managing and functional utilities. Each file relates to one particular functional utility.

  • color.py It contains some color managing code to provide some convenient APIs to get some plotting colors.

  • error.py It contains a Reporter class to manage the run-time errors. It will record the message of the error/warning and report the final statistics of the whole process.

  • file.py Some file-related operations, such as JSON reading and dumping, directory deleting and list dumping.

  • GloVe.py Actual launching file for GloVe embedding training. It relies on the glove module and runs under Python 2.7. It receives the whole sequence matrix and output the word embedding matrix, which will be saved as a NumPy file.

  • init.py Model initialization functions.

  • magic.py Randomization implementation utilities, such as random seed, random list and sampling.

  • manager.py Some useful managers to simplify the operations.

    • PathManager Dataset path manager. Given the dataset name, it will automatically read the dataset bases from runConfig.py and generate a series of paths, such as data file path(given the subdataset type), word embedding path, sequence length path, running document path and etc.
    • TrainStatManager Statistics manager for training/validating experiments. It can record the accuracy/loss and epoch time consuming data during training/validating. Besides, it will save the best-performed model state dictionary after validating. At last, it can report the overall training statistics.
    • TestStatManager Statistics manager for testing experiments. It has similar functions as TrainStatManager but used in testing stage.
    • TrainConfigManager Configuration reading manager of runConfig.json or testConfig.json. It integrates some parameters together and return them as a whole part.

  • matrix.py Some torch matrix-related functions, such as batch-dot and matrix reduce.

  • plot.py It contains a Visdom visualization class VisdomPlot and a line plot function of matplotlib. Visdom interactions can be done through the static class.

  • stat.py Some statistic utility function like parameter statistics and confidence interval calculation.

  • timer.py A step timer class that compute the step time interval to record the time slice between consecutive validating or testing displays and report the estimated remaining running time.

  • training.py Some utility functions called during training, like making sequence batch, parse the task parameters(shot,way...), dynamic routing, sequence masking and etc.

  • zip.py Utility functions to unzip the zipped files.

components

Many neural network and meta-learning components including sampler, episode task, dataset entity classes, training procedures, sequence embeddings and etc.

  • datasets.py It contains the torch dataset entity class SeqFileDataset, which reads the sequence data in a single file into the memory. It also reads in the sequence length information to output the sequence and length in pair.

  • sampler.py It contains the episode-training task sampler class EpisodeSampler. Given the label space(sampled classes) and class-wise seed, it samples support set and query set from the dataset and keep these two sets non-overlapped.

  • task.py Some model-dependent task entity classes which integrates label space sampling, class-wise sampling(by generating class seeds for samplers), dataloader instantiation(use the batch making utility function in utils/training.py) and label normalization. It leaves a function episode() to be implemented for different models, which randomly samples a task from the dataset and returns the episode data as support/query set form.

  • procedure.py Detailed training procedures for different kind of models, such as fast-adaption based(MAML family), meta-loss-optimization based(mostly metric-based), infinite mixture prototypes based(IMP and our SIMPLE) and etc. These methods have varied ways to forward the model or compute the loss value, so they may have different implementations.