Blended threat prediction based on knowledge graph embedding in the IoBE

ICT Express - Tập 9 - Trang 903-908 - 2023
Minkyung Lee1, Deuk-Hun Kim2, Julian Jang-Jaccard3, Jin Kwak4
1ISAA Lab., Department of Cyber Security, Ajou University, Suwon, Republic of Korea
2Institute for Information and Communication, Ajou University, Suwon, Republic of Korea
3Comp Sci/Info Tech, Massey University, Auckland 0632, New Zealand
4Department of Cyber Security, Ajou University, Suwon, Republic of Korea

Tài liệu tham khảo

Zhang, 2017, Security and privacy in smart city applications: Challenges and solutions, IEEE Commun. Mag., 55, 122, 10.1109/MCOM.2017.1600267CM Alenezi, 2020, On the relationship between software complexity and security, Int. J. Softw. Eng. Appl., 11, 51 Y. Mirsky, T. Mahelr, I. Shelef, Y. Elovici, CT-GAN: Malicious tampering of 3D medical imagery using deep learning, in: 28th USENIX Security Symp. Berkeley, USA, 2018, pp. 461–478. Westerlund, 2019, The emergence of deepfake technology: A review, Technol. Innov. Manage. Rev., 9, 39, 10.22215/timreview/1282 Lee, 2022, Novel architecture of security orchestration, automation and response in internet of blended environment, Comput. Mater. Contin., 73, 199 J. Liu, B. Liu, R. Zhang, C. Wang, Multi-step attack scenarios mining based on neural network and Bayesian network attack graph, in: Proceedings of International Conference on Artificial Intelligence and Security, New York, USA, 2019, pp. 62–74. Angelini, 2019, An attack graph-based on-line multi-step attack detector, IEEE Access, 8, 1031 S. Ingale, M. Paraye, D. Ambawade, A survey on methodologies for multi-step attack prediction, in: 2020 International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2020, pp. 37–45. Navarro, 2018, A systematic survey on multi-step attack detection, Comput. Secur., 76, 214, 10.1016/j.cose.2018.03.001 M. Iannacone, S. Bohn, G. Nakamura, J. Gerth, K. Huffer, R. Bridges, E. Ferragut, J. Goodall, Developing an ontology for cyber security knowledge graphs, in: Proceedings of the 10th Annual Cyber and Information Security Research Conference, Vol. 12, 2015, pp. 1–4. Wang, 2021, Social engineering in cybersecurity: A domain ontology and knowledge graph application examples, Cybersecurity, 4, 10.1186/s42400-021-00094-6 S.N. Narayanan, A. Ganasan, K. Joshi, T. Oates, A. Joshi, T. Finin, Early detection of cybersecurity threats using collaborative cognition, in: Proceedings of the IEEE 4th International Conference on Collaboration and Internet Computing, Philadelphia, PA, USA, 2018, pp. 354–363. Z. Han, X. Li, H. Liu, Z. Xing, Z. Feng, DeepWeak: Reasoning common software weaknesses via knowledge graph embedding, in: 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), Campobasso, Italy, 2018, pp. 456–466. H. Xiao, Z. Xing, X. Li, H. Guo, Embeddings and predicting software security entity relationships: A knowledge graph based approach, in: 2019 International Conference on Neural Information Processing, Vol. 11955, 2019, pp. 50–63. J. Pujara, H. Miao, L. Getoor, W. Cohen, Knowledge graph identification, in: International Semantic Web Conference, 2013, pp. 542–557. X. Zou, A survey on application of knowledge graph, in: International Conference on Control Engineering and Artificial Intelligence, Singapore, Vol. 1487, 2020. Wang, 2021, A survey on knowledge graph embeddings for link prediction, Symmetry, 13, 485, 10.3390/sym13030485 Mohamed, 2019, Loss functions in knowledge graph embeddings models, DL4KG@ESWC, 2377, 1 A. Bordes, N. Usunier, A. Garia-Duran, J. Weston, O. Yaknenko, Translating embeddings for modeling multi-relational data, in: Proceedings of the NIPS, Lake Tahoe, NV, USA, 2013, pp. 5–8. Z. Wang, J. Zhang, J. Feng, Z. Chen, Knowledge graph embedding by translating on hyperplanes, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 28, 2014, (1). Y. Lin, Z. Liu, M. Sun, Y. Liu, X. Zhu, Learning entity and relation embeddings for knowledge graph completion, in: Proceedings of the AAAI Conference on Artificial Intelligence. 29, 2015, (1). M. Nickel, V. Tresp, H.P. Kreigel, A three-way model for collective learning on multi-relational data, in: International Conference on Machine Learning, 2011. B. Yang, W. Yih, X. He, J. Gao, L. Deng, Embeddings entities and relations for learning and inference in knowledge bases, in: Proceedings of the International Conference on Learning, 2015. W. Wang, Z. Xie, J. Liu, Y. Duan, B. Huang, J. Zhang, MDistMult: A multiple scoring functions model for link prediction on antiviral drugs knowledge graph, in: 2021 IEEE 23rd International Conference on High Performance Computing & Communications; 7th International Conference on Data Science & Systems; 19th International Conference on Smart City; 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Haikou, Hainan, China, 2021, pp. 2042–2049. T. Trouillon, J. Welbl, S. Riedel, E. Gaussier, G. Bouchard, Complex embeddings for simple link prediction, in: Proceedings of the 33rd International Conference on Machine Learning, Vol. 48, 2016, pp. 2071–2080. OASIS, Introduction to STIX, [Online]. Available: https://oasis-open.github.io/cti-documentation/stix/intro.html. OASIS, 2020 X. Han, S. Cao, X. Lv, Y. Lin, Z. Liu, M. Sun, J. Li, OpenKE: An open toolkit for knowledge embedding, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Brussels, Belgium, 2018, pp. 139–144. D. Grochocki, J.H. Huh, R. Berthier, R. Bobba, W.H. Sanders, A.A. Cárdenas, J.G. Jetcheva, AMI threats intrusion detection requirements and deployment recommendations, in: IEEE Third International. Conference on Smart Grid Communications, Tainan, Taiwan, 2012, pp. 395–400. GitHub, ATT & CK STIX Data, [Online]. Available: https://github.com/mitre-attack/attack-stix-data. Craswell, 2009, 1703 GitHub, KG-BERT:BERT for knowledge graph completion, [Online]. Available: https://github.com/yao8839836/kg-bert. Desai, 2017 Buchka, 2018