Generating Attack Data in the Internet of Things using Generative Adversarial Networks

• Iran F. Ribeiro UFES
• Guilherme S. G. Brotto UFES
• Giovanni Comarela UFES
• Vinícius F. S. Mota UFES

DOI: https://doi.org/10.5753/courb.2024.3377

Abstract

Analyzing the data generated by gadgets is crucial for spotting and minimizing cyberattacks on the Internet of Things. However, public data representing real attacks still tends to be scarce. To increase data availability, this work presents a study on the use of Generative Adversarial Networks (GANs) to generate synthetic attack data on IoT devices with high fidelity to real data, i.e., with similar characteristics. Simultaneously, ensuring privacy and that the utility of synthetic data in machine learning tasks is similar to real data. For this purpose, two GAN models, CTGAN and NetShare, were compared using a dataset containing normal traffic and attacks on IoT devices. The results indicate that both GAN models are efficient in generating synthetic data, both in fidelity and quality. However, CTGAN proves to be the most efficient model, considering execution time and memory consumption.

References

Aleroud, A., Yang, F., Pallaprolu, S. C., Chen, Z., and Karabatis, G. (2021). Anonymization of network traces data through condensation-based differential privacy. Digital Threats: Research and Practice (DTRAP), 2(4):1–23.

Alex, C., Creado, G., Almobaideen, W., Alghanam, O. A., and Saadeh, M. (2023). A comprehensive survey for iot security datasets taxonomy, classification and machine learning mechanisms. Computers & Security, page 103283.

Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein generative adversarial networks. In International conference on machine learning, pages 214–223. PMLR.

Borji, A. (2022). Pros and cons of gan evaluation measures: New developments. Computer Vision and Image Understanding, 215:103329.

Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale gan training for high fidelity natural image synthesis.

Brophy, E., Wang, Z., She, Q., and Ward, T. (2023). Generative adversarial networks in time series: A systematic literature review. ACM Computing Surveys, 55(10):1–31.

Cunha, V. C., Zavala, A. Z., Magoni, D., Inácio, P. R. M., and Freire, M. M. (2022). A complete review on the application of statistical methods for evaluating internet traffic usage. IEEE Access, 10:128433–128455.

Dash, A., Ye, J., and Wang, G. (2023). A review of generative adversarial networks (gans) and its applications in a wide variety of disciplines: From medical to remote sensing. IEEE Access.

Esteban, C., Hyland, S. L., and Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633.

Gheisari, M., Alzubi, J., Zhang, X., Kose, U., and Saucedo, J. A. M. (2020). A new algorithm for optimization of quality of service in peer to peer wireless mesh networks. Wireless Networks, 26:4965–4973.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680.

Hossain, M. D., Ochiai, H., Doudou, F., and Kadobayashi, Y. (2020). Ssh and ftp brute-force attacks detection in computer networks: Lstm and machine learning approaches. In 2020 5th international conference on computer and communication systems (ICCCS), pages 491–497. IEEE.

Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation.

Kingma, D. P. and Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.

Kumar, V. and Sinha, D. (2023). Synthetic attack data generation model applying generative adversarial network for intrusion detection. Computers & Security, 125:103054.

Marani, A. and Nehdi, M. L. (2022). Predicting shear strength of frp-reinforced con-crete beams using novel synthetic data driven deep learning. Engineering Structures, 257:114083.

Nekvi, R. I., Saha, S., Al Mtawa, Y., and Haque, A. (2023). Examining generative adversarial network for smart home ddos traffic generation. In 2023 International Symposium on Networks, Computers and Communications (ISNCC), pages 1–6. IEEE.

Pundir, M., Sandhu, J. K., and Kumar, A. (2021). Quality-of-service prediction techniques for wireless sensor networks. In Journal of Physics: Conference Series, volume 1950, page 012082. IOP Publishing.

Qian, C., Yu, W., Lu, C., Griffith, D., and Golmie, N. (2022). Toward generative adversarial networks for the industrial internet of things. IEEE Internet of Things Journal, 9(19):19147–19159.

Sebastian Garcia, Agustin Parmisano, . M. J. E. (2020). Iot-23: A labeled dataset with malicious and benign iot network traffic (version 1.0.0) [data set].

Shahid, M. R., Blanc, G., Jmila, H., Zhang, Z., and Debar, H. (2020). Generative deep learning for internet of things network traffic generation. In Pacific Rim International Symposium on Dependable Computing, pages 70–79. IEEE.

Sharafaldin, I., Gharib, A., Lashkari, A. H., and Ghorbani, A. A. (2018). Towards a reliable intrusion detection benchmark dataset. Software Networking, 2018(1):177–200.

Wang, Z., She, Q., and Ward, T. E. (2021). Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR), 54(2):1–38.

Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. Advances in neural information processing systems, 32.

Yin, Y., Lin, Z., Jin, M., Fanti, G., and Sekar, V. (2022). Practical gan-based synthetic ip header trace generation using netshare. In Proceedings of the ACM SIGCOMM 2022 Conference, pages 458–472.