Abstract
Power systems have been attracting the attention of attackers because of its great value. Identifying attack intentions is essential for proactively blocking the intrusion into power information systems. In this paper, we propose AIGCN, a novel attack intention detection model based on graph convolutional networks. Particularly, AIGCN first presents an abnormal IP detection method based on log behavior analysis to filter suspicious malicious IPs. And then AIGCN models the interactive relationships between suspicious IPs as a graph and performs graph convolution operation on the graph to effectively detect the attack intentions and learn the attack patterns with different intentions. Experimental results on real-world datasets verify that AIGCN outperforms baseline methods in detecting attack intentions and demystifying corresponding attack patterns.
Similar content being viewed by others
References
Nazir, M., Enslin, J. H., & Babakmehr, M. (2020). Power system protection response under geomagnetically induced currents. In: 2020 Clemson University Power Systems Conference (PSC), IEEE, pp 1–6.
Gao, Y., Iqbal, S., Zhang, P., & Qiu, M. (2015). Performance and power analysis of high-density multi-GPGPU architectures: A preliminary case study. In: IEEE 17th HPCC, pp 29–35.
Qiu, M., Ming, Z., Li, J., Liu, S., Wang, B., & Lu, Z. (2012). Three-phase time-aware energy minimization with dvfs and unrolling for chip multiprocessors, 58(10), 439–445.
Niu, J., Liu, C., et al. (2013). Energy efficient task assignment with guaranteed probability satisfying timing constraints for embedded systems. IEEE Transaction on Parallel and Distributed Systems, 25(8), 2043–2052.
Qiu, M., Khisamutdinov, E., et al. (2013a). Rna nanotechnology for computer design and in vivo computation.
Lu, R., Jin, X., Zhang, S., Qiu, M., & Wu, X. (2018). A study on big knowledge and its engineering issues. IEEE Transactions on Knowledge and Data Engineering, 31(9), 1630–1644.
Tao, L., Golikov, S., et al. (2015). A reusable software component for integrated syntax and semantic validation for services computing. In: IEEE Symposium on Service-Oriented System Engineering (SOSE), pp 127–132.
Zhang, K., Kong, J., Qiu, M., & Song, G. (2005). Multimedia layout adaptation through grammatical specifications, 10(3), 245–260.
Gai, K., Qiu, M., Chen, L., & Liu, M. (2015a). Electronic health record error prevention approach using ontology in big data. In: IEEE 17th HPCC conference.
Zhao, H., Chen, M., Qiu, M., Gai, K., & Liu, M. (2016). A novel pre-cache schema for high performance android system. Future Generation Computer Systems, 56, 766–772.
Li, J., Qiu, M., Niu, J., et al. (2010) Feedback dynamic algorithms for preemptable job scheduling in cloud systems. In: IEEE/WIC/ACM conference on Web Intelligence.
Qiu, M., Ming, Z., Li, J., Liu, J., Quan, G., & Zhu, Y. (2013b). Informer homed routing fault tolerance mechanism for wireless sensor networks. Journal of Systems Architecture, 59(4–5):260–270.
Zhang, Z., Wu, J., Deng, J., & Qiu, M. (2008). Jamming ack attack to wireless networks and a mitigation approach. In: IEEE GLOBECOM, pp 1–5.
Radmanesh, H., & Kavousi, A. (2017). Aircraft electrical power distribution system protection using smart circuit breaker. IEEE Aerospace and Electronic Systems Magazine, 32(1), 30–40.
Ahmed, A. A., & Mohammed, M. F. (2018). Sairf: A similarity approach for attack intention recognition using fuzzy min-max neural network. Journal of Computational Science, 25, 467–473.
Impram, S., Nese, S. V., & Oral, B. (2020). Challenges of renewable energy penetration on power system flexibility: A survey. Energy Strategy Reviews, 31,.
Su, H., Qiu, M., & Wang, H. (2012). Secure wireless communication system for smart grid with rechargeable electric vehicles. IEEE Communications Magazine, 50(8), 62–68.
Tang, X., Li, K., et al. (2012). A hierarchical reliability-driven scheduling algorithm in grid systems. Journal of Parallel and Distributed Computing, 72(4), 525–535.
Sun, W., Wang, Q., Li, M., & Ni, M. (2020). Extreme risk assessment in power system considering cyber attacks. In: 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), pp 766–770.
Hu, H., Liu, J., Zhang, Y., Liu, Y., Xu, X., & Huang, J. (2020). Attack scenario reconstruction approach using attack graph and alert data mining. Journal of Information Security and Applications, 54,.
Qiu, H., Qiu, M., Memmi, G., Ming, Z., & Liu, M. (2018). A dynamic scalable blockchain based communication architecture for IoT. In: International Conference on Smart Blockchain, pp 159–166.
Jin, D., Lu, Y., Qin, J., Cheng, Z., & Mao, Z. (2020). Swiftids: Real-time intrusion detection system based on lightgbm and parallel intrusion detection mechanism. Computers & Security, 97,.
Ning, P., Cui, Y., & Reeves, D. S. (2002). Constructing attack scenarios through correlation of intrusion alerts. In: Proceedings of the 9th ACM Conference on Computer and Communications Security, pp 245–254.
Cheung, S., Lindqvist, U., & Fong, M. W. (2003). Modeling multistep cyber attacks for scenario recognition. Proceedings DARPA Information Survivability Conference And Exposition, IEEE, 1, 284–292.
Gai, K., Qiu, M., Sun, X., & Zhao, H. (2016b). Security and privacy issues: A survey on FinTech. In: SmartCom, pp 236–247.
Thakur, K., Qiu, M., Gai, K., & Ali, M. L. (2015). An investigation on cyber security threats and security models. In: CSCloud’15, pp 307–311.
Ahmed, A. A., & Zaman, N. A. K. (2017). Attack intention recognition: A review. International Journal of Network Security, 19(2), 244–250.
Gai, K., Qiu, M., Thuraisingham, B., & Tao, L. (2015b). Proactive attribute-based secure data schema for mobile cloud in financial industry. In: IEEE 17th HPCC.
Gai, K., Qiu, M., & Elnagdy, S. (2016a). A novel secure big data cyber incident analytics framework for cloud-based cybersecurity insurance. In: IEEE BigDataSecurity conference.
Gai, K., Qiu, M., Zhao, H., & Xiong, J. (2016c). Privacy-aware adaptive data encryption strategy of big data in cloud computing. In: IEEE 3rd CSCloud conference.
Guo, Y., Zhuge, Q., Hu, J., et al. (2011). Optimal data allocation for scratch-pad memory on embedded multi-core systems[C]. International Conference on Parallel Processing. IEEE. 464-471.
Guo, Y., Zhuge, Q., Hu, J., et al. (2013). Data placement and duplication for embedded multicore systems with scratch pad memory. IEEE Transactions on CAD.
Ahmed, A. A. (2020). Investigation approach for network attack intention recognition. In: Digital Forensics and Forensic Investigations: Breakthroughs in Research and Practice, IGI Global, pp 185–208.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781.
Kipf, T. N., Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:160902907.
Devlin, J., Chang, M. W., Lee, K., Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805
Haas, S., & Fischer, M. (2018). Gac: graph-based alert correlation for the detection of distributed multi-step attacks. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp 979–988.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection on Big Data Security Track
Rights and permissions
About this article
Cite this article
Tang, Q., Chen, H., Ge, B. et al. AIGCN: Attack Intention Detection for Power System Using Graph Convolutional Networks. J Sign Process Syst 94, 1119–1127 (2022). https://doi.org/10.1007/s11265-021-01724-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-021-01724-5