Abstract
In automated penetration testing (APT), agents are tasked with identifying attack targets and formulating appropriate action plans within partially-observed network environments. The reasoning over the network based on the information gathering from reconnaissance is essential. However, existing reasoning methods show considerable neglect for computer networks and their unique characteristics. Additionally, despite Graph Neural Networks (GNNs) demonstrated efficacy in modeling graph structures, the scarcity of adequately labeled network data adds complexity to the training of GNNs. We present a novel method, termed Graph Pre-training for Reconnaissance Perception in Automated Penetration Testing (GPRP). This pioneering approach is designed to learn the invariant properties entailed in the structures and semantics of the computer networks from an extensive set of unlabeled and synthetic data during pre-training. Consequently, the resulting pre-trained model could swiftly adapt to target networks, after undergoing fine-tuning with very few network observations, and exhibits enhanced capabilities in reasoning network properties. Extensive experiments on both customized and FatTree networks articulate the efficacy of our model in tasks centered around network reasoning, such as node classification and link prediction tasks. Further verification of GPRP in a real-world local area network, underscores the practical usage of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. ACM SIGCOMM Comput. Commun. Rev. 38(4), 63–74 (2008)
Chen, K., Lu, H., Fang, B., Sun, Y., Su, S., Tian, Z.: Survey on automated penetration testing technology research. J. Softw. 35(5), 2268–2288 (2023)
Group, O., et al.: Information systems security assessment framework. Open Information Systems Security Group (2006)
Hu, Z., Beuran, R., Tan, Y.: Automated penetration testing using deep reinforcement learning. In: 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pp. 2–10. IEEE (2020)
Hu, Z., Dong, Y., Wang, K., Chang, K.W., Sun, Y.: GPT-GNN: generative pretraining of graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1857–1867 (2020)
Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. In: Proceedings of the Web Conference 2020, pp. 2704–2710 (2020)
Hutchins, E.M., Cloppert, M.J., Amin, R.M., et al.: Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. Lead. Isues Inf. Warfare Secur. Res. 1(1), 80 (2011)
Jha, S., Sheyner, O., Wing, J.: Two formal analyses of attack graphs. In: Proceedings 15th IEEE Computer Security Foundations Workshop. CSFW-15, pp. 49–63. IEEE (2002)
Kim, M., Leskovec, J.: The network completion problem: Inferring missing nodes and edges in networks. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 47–58. SIAM (2011)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016). arXiv preprint arXiv:1609.02907
Koskinen, J.H., Robins, G.L., Wang, P., Pattison, P.E.: Bayesian analysis for partially observed network data, missing ties, attributes and actors. Soc. Netw. 35(4), 514–527 (2013)
Latecki, L.P.V.C.G.P.J.: Graph convolutional networks based on manifold learning for semi-supervised image classification. Comput. Vis. Image Underst. 277, 103618 (2023)
Li, H., Wang, X., Zhang, Z., Zhu, W.: Out-of-distribution generalization on graphs: A survey (2022). arXiv preprint arXiv:2202.07987
Li, Q., Hu, M., Hao, H., Zhang, M., Li, Y.: Innes: an intelligent network penetration testing model based on deep reinforcement learning. Appl. Intell. 53(22), 27110–27127 (2023)
Li, X., et al.: Graph neural network with curriculum learning for imbalanced node classification. Neurocomputing 574, 127229 (2024)
Liu, S., Feng, Y., Wu, K., Cheng, G., Huang, J., Liu, Z.: Graph-attention-based casual discovery with trust region-navigated clipping policy optimization. IEEE Trans. Cybern. 53, 2311–2324 (2021)
Sarraute, C.: Automated attack planning (2013). arXiv preprint arXiv:1307.7808
Strom, B.E., Applebaum, A., Miller, D.P., Nickels, K.C., Pennington, A.G., Thomas, C.B.: MITRE ATT&CK: Design and philosophy. In: Technical report. The MITRE Corporation (2018)
Team, P., et al.: The penetration testing execution standard documentation (2017)
Tran, C., Shin, W.Y., Spitz, A., Gertz, M.: DeepNC: deep generative network completion. IEEE Trans. Pattern Anal. Mach. Intell. 44(4), 1837–1852 (2020)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. stat 1050(20), 10–48550 (2017)
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., Yu, P.S.: Heterogeneous graph attention network. In: The World Wide Web Conference, pp. 2022–2032 (2019)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2020)
Xia, J., Zhu, Y., Du, Y., Li, S.Z.: A survey of pretraining on graphs: Taxonomy, methods, and applications (2022). arXiv preprint arXiv:2202.07893
Xing, B., Gao, L., Sun, J., Yang, W.: Design and implementation of automated penetration testing system. Application Research of Computers (2010)
Zennaro, F.M., Erdődi, L.: Modelling penetration testing with reinforcement learning using capture-the-flag challenges: trade-offs between model-free learning and a priori knowledge. IET Inf. Secur. 17(3), 441–457 (2023)
Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI open 1, 57–81 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Y. et al. (2024). Graph Pre-training for Reconnaissance Perception in Automated Penetration Testing. In: Huang, DS., Si, Z., Pan, Y. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14864. Springer, Singapore. https://doi.org/10.1007/978-981-97-5588-2_26
Download citation
DOI: https://doi.org/10.1007/978-981-97-5588-2_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5587-5
Online ISBN: 978-981-97-5588-2
eBook Packages: Computer ScienceComputer Science (R0)