A Large Scale Malware Testbed
- Manage a set of VMware virtual machines
-
Start from a snapshot (before testing malware)
-
Run program in the virtual machine
-
Automate large scale malware / software testing
-
Collecting execution behavior, e.g. system calls
-
Control malware / software execution
-
Hypervisor: VMware Workstation
-
Virturl machine: Linux Ubuntu 12.04
-
Host: Linux Ubuntu 12.04
-
python 2.7
-
copy scripts under folder
vmto virtual machine $HOME folder -
customize parameters in the beginning of
host/run_app_in_host.py -
customize test cases (i.e. malware) in
host/apps/examples.txt
python host/run_app_in_host.py
- If you enjoy this work, please cite the following paper.
Sun, R., Sapountzis, N., Yuan, X., Botacin, M., Bishop, M., Porter, D.E., Li, X., Gregio, A. and Oliveira, D., 2020, April. The dose makes the poison—Leveraging uncertainty for effective malware detection. In IEEE Transaction of Dependable and Secure Computing. IEEE.
@inproceedings{sun2020defense,
title={A Praise for Defensive Programming: Leveraging Uncertainty for Effective Malware Mitigation},
author={Sun, Ruimin and Sapountzis, Nikolaos and Yuan, Xiaoyong and Botacin, Marcus and Bishop, Matt and Porter, Donald E and Li, Xiaolin and Gregio, Andre and Oliveira, Daniela},
booktitle={Transaction of Dependable and Secure Computing, 2020 IEEE},
organization={IEEE}
}
Sun, R., Yuan, X., Lee, A., Bishop, M., Porter, D.E., Li, X., Gregio, A. and Oliveira, D., 2017, August. The dose makes the poison—Leveraging uncertainty for effective malware detection. In Dependable and Secure Computing, 2017 IEEE Conference on (pp. 123-130). IEEE.
@inproceedings{sun2017dose,
title={The dose makes the poison—Leveraging uncertainty for effective malware detection},
author={Sun, Ruimin and Yuan, Xiaoyong and Lee, Andrew and Bishop, Matt and Porter, Donald E and Li, Xiaolin and Gregio, Andre and Oliveira, Daniela},
booktitle={Dependable and Secure Computing, 2017 IEEE Conference on},
pages={123--130},
year={2017},
organization={IEEE}
}
Ruimin Sun