This repository contains the implementation of our paper "MultiEvasion: Evasion Attacks Against Multiple Malware Detectors". In this paper, we proposed evasion attacks "MultiEvasion" that can evade multiple DNNs based malware detectors. We leveraged different adversarial example generation methods, like FGSM, FFGSM, PGD, which orginally introduced in image domain, we customized them to apply on obfuscating malware programs.
** The data set and code for research purpose only**
When reporting results that use the dataset or code in this repository, please cite the paper below:
Hao Liu, Wenhai Sun, Nan Niu, Boyang Wang, "MultiEvasion: Evasion Attacks Against Multiple Malware Detectors" by IEEE Conference on Communications and Network Security (CNS 2022), Austin, TX, USA, 3-5 October 2022
To running this repositery, we recommed you to install the environment with advmal.txt file:
conda env create --file advmal.txt
This repository contains separate directories, including "attacks", "src". A brief description of the contents of these directories is below. More detailed usage instructions are found in the following contents.
The attacks directory contains the scripts for generating adversarial malware with different adversarial algorithms and function manipulations.
The src directory contains the scipts for trianing and testing deep learning based malware detectors, evaluating on evading multiple malware detectors (with same/different input format (e.g., raw bytes or images)) at the same time.
We evaluated our method over two datasets.
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One is public dataset, named phd dataset, contains 977 benign PE programs and 2597 malicious PE programs. You can access the dataset through "https://github.com/tgrzinic/phd-dataset".
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The other one is collected by us, named ME dataset, contains 1192 benign PE programs across multiple Windows versions (e.g., Windows XP, Vista, 8 and 10) in total, we randomly selected 1000 for all experiments in this paper, and 1000 malicious PE programs downloaded from VirusShare. You can access the download link through https://mailuc-my.sharepoint.com/:f:/g/personal/liu3ho_mail_uc_edu/Evbpfh-gwc9EnBHB9c1ysVIBfgfvOhScnRsfTyfhHOCdPw?e=2dajBM. Please contact us to renew the link if the current link expired.
Please see the details of parameters in each script.
python3 train_models.py --model_name=malconv --input_size=102400 --window_size=500 --batch_size=32 --epochs=50 --lr=0.0001 --num_workers=1 --log_file_path=../result/inputsize_102400/train_log.txt --checkpoint_dir=../checkpoint/inputsize_102400/ --train_label_path=../data/train_data_label.csv --test_label_path=../data/test_data_label.csv --val_label_path=../data/val_data_label.csv --all_file_path=../data/all_file/
--model_name: name of the model, include "malconv", "fireeye" and "AvastNet"
--input_size: input size of models
--window_size: stride size
--batch_size: batch size
--epochs: number of epochs to train models
--lr: learning rate
--num_workers: number of workers to load data
--log_file_path: file path to save the results
--checkpoint_dir: parent folder path to save model or load model
--train_label_path: csv file that includes file names and the corresponding labels of training data
--test_label_path: csv file that includes file names and the corresponding labels of testing data
--val_label_path: csv file that includes file names and the corresponding labels of validation data
--all_file_path: folder path that includes all files (pe programs), which include both benign and malcious pe programs
python3 train_image_malware_detector.py --epochs=50 --batch_size=128 --model_name=ResNet18 --lr=0.001 --image_resolution=320 --adjust_lr_flag=False --image_path=../data/all_image/ --model_save_path=../checkpoint/model_image_.pth --log_file_name=../result/model_training/log_image_.txt --train_label_table_path=../data/train_data_label.csv --test_label_table_path=../data/test_data_label.csv --val_label_table_path=../data/val_data_label.csv
--epochs: number of epochs
--batch_size: batch size
--model_name: model name, default is ResNet18
--lr: learning rate
--image_resolution: the size of width (or height). height is equal to width here
--model_save_path: path to save trained model
--log_file_path: file path to save the results
--adjust_lr_flag: bool value, whether adjusting learning rate during training process
--image_path: folder path that includes all grayscale images of all benign and malicious pe programs
--train_label_path: csv file that includes file names and the corresponding labels of training data
--test_label_table_path: csv file that includes file names and the corresponding labels of testing data
--val_label_table_path: csv file that includes file names and the corresponding labels of validation data
python3 adv_attack_against_detectors.py --adversary=FGSM --eps=0.4 --alpha=0.7 --iter_steps=20 --partial_dos=False --content_shift=True --slack=True --combine_w_slack=False --log_file_for_result=../result/same_input_format/fgsm_0.4/result_file.txt --preferable_extension_amount=0 --preferable_shift_amount=4096 --test_data_path=../data/all_file/ --test_label_path=../data/test_mal_label.csv --model_path_1=../checkpoint/malconv_model.pth --model_path_2=../checkpoint/fireeye_model.pth --use_cpu=1 --batch_size=1 --first_n_byte=102400 --window_size=500
--adversary: name of adversary, include "FGSM", "FFGSM" and "PGD"
--eps: control the perturbation size, range [0,1]
--alpha: the maximum perturbation size of each iteration (for PGD)
--iter_steps: number of iteration steps to perform evasion attacks (for PGD)
--partial_dos: bool value, if True, select Partial DOS as function manipulation
--content_shift: bool value, if True, select Content Shift as function manipulation
--slack: bool value, if True, select Slack as function manipulation
--combine_w_slack: bool value, if True, add Slack as an additional function manipulation
--log_file_result: file path the save the results
--preferable_extension_amount: the number of bytes to perform Extension function manipulation (should be multiple of 512)
--preferable_shit_amount: the number of bytes to perform Content Shift function manupulation (should be multiple of 512)
--test_data_path: the file path that includes all testing data (testing pe programs)
--test_label_path: csv file that includes file names and the corresponding labels of testing data
--model_path_1: the trained model path of model 1 (default is MalConv)
--model_path_1: the trained model path of model 2 (default is FireEyeNet)
--batch_size: batch size
--use_cpu: number of workers/cpu cores to load data
--first_n_byte: input size of models
--window_size: the stride size of models
How to select function manipulations, please follow the below parameter combinations to choose function manipulations (preferable_extension_amount and preferable_shift_amount can be altered to other value of multiple of 512 when they are not 0)
Partial DOS:
--partial_dos=True --content_shift=False --slack=False --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=0
Content Shift:
--partial_dos=False --content_shift=True --slack=False --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=512
Slack:
--partial_dos=False --content_shift=False --slack=True --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=0
Partial DOS + Content Shift:
--partial_dos=True --content_shift=True --slack=False --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=512
Partial DOS + Slack:
--partial_dos=True --content_shift=False --slack=True --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=0
Content Shift + Slack:
--partial_dos=False --content_shift=True --slack=True --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=512
Partial DOS + Content Shift + Slack:
--partial_dos=True --content_shift=True --slack=True --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=512
Full DOS:
--partial_dos=False --content_shift=False --slack=False --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=0
Extension:
--partial_dos=False --content_shift=False --slack=False --combine_w_slack=False --preferable_extension_amount=512 --preferable_shift_amount=0
Full DOS + Content Shift:
--partial_dos=False --content_shift=False --slack=False --combine_w_slack=False --preferable_extension_amount=0 --preferable_shift_amount=512
Full DOS + Slack:
--partial_dos=False --content_shift=False --slack=False --combine_w_slack=True --preferable_extension_amount=0 --preferable_shift_amount=0
Extension + Content Shift:
--partial_dos=False --content_shift=False --slack=False --combine_w_slack=False --preferable_extension_amount=512 --preferable_shift_amount=512
Extension + Slack:
--partial_dos=False --content_shift=False --slack=False --combine_w_slack=True --preferable_extension_amount=32768 --preferable_shift_amount=0
Full DOS + Content Shift + Slack:
--partial_dos=False --content_shift=False --slack=False --combine_w_slack=True --preferable_extension_amount=0 --preferable_shift_amount=32768
Extension + Content Shift + Slack:
--partial_dos=False --content_shift=False --slack=False --combine_w_slack=True --preferable_extension_amount=512 --preferable_shift_amount=512
Evaluation of MultiEvasion against three malware detectors (MalConv, FireEyeNet and AvastNet) at the same time
python3 adv_attack_against_3detectors.py --adversary=FGSM --eps=0.4 --alpha=0.7 --iter_steps=20 --partial_dos=False --content_shift=True --slack=True --combine_w_slack=False --log_file_for_result=../result/same_input_format_3detectors/fgsm_0.9/result_file.txt --preferable_extension_amount=0 --preferable_shift_amount=4096 --test_data_path=../data/all_file/ --test_label_path=../data/test_mal_label.csv --model_path_1=../checkpoint/malconv_model.pth --model_path_2=../checkpoint/fireeye_model.pth --model_path_3=../checkpoint/AvastNet_model.pth --use_cpu=1 --batch_size=1 --first_n_byte=102400 --window_size=500
The parameters decriptions same as above.
Evaluation of MultiEvasion against two malware detectors with different input formats(MalConv and ResNet18) at the same time
python3 adv_attack_against_detectors_different_input.py --adversary=FGSM --eps=0.4 --alpha=0.7 --iter_steps=20 --partial_dos=False --content_shift=True --slack=True --combine_w_slack=False --log_file_for_result=../result/different_input/fgsm0.4/result_file.txt --preferable_extension_amount=0 --preferable_shift_amount=4096 --width=320 --height=320 --test_data_path=../data/all_file/ --test_label_path=../data/test_mal_label.csv --model_path_1=../checkpoint/malconv_model.pth --model_path_2=../checkpoint/ResNet18.pth --use_cpu=1 --batch_size=1 --first_n_byte=102400 --window_size=500
The most parameters decriptions same as above.
--width: width value of image. Default is 320.
--height: height value of image. Default is 320.
Hao Liu, liu3ho@mail.uc.edu
Boyang Wang, boyang.wang@uc.edu