Computer Science ›› 2023, Vol. 50 ›› Issue (9): 68-74.doi: 10.11896/jsjkx.230500233

• Data Security • Previous Articles     Next Articles

Study on Dual-security Knowledge Graph for Process Industrial Control

WANG Jing, ZHANG Miao, LIU Yang, LI Haoling, LI Haotian, WANG Bailing, WEI Yuliang   

  1. School of Computer Science and Technology,Harbin Institute of Technology(Weihai),Weihai,Shandong 264209,China
  • Received:2023-05-31 Revised:2023-07-08 Online:2023-09-15 Published:2023-09-01
  • About author:WANG Jing,born in 2001,postgra-duate.His main research interests include natural language processing and data mining.
    WEI Yuliang,born in 1989,assistant researcher.His main research interests include natural language processing,industrial Internet security and data mi-ning.
  • Supported by:
    National Key R & D Program of China(2021YFB2012400).

Abstract: With the development of industrial control systems,security issues in these systems have become increasingly important.However,traditional industrial safety systems usually focus on either information security or production safety,thus failing to consider both issues at the same time.As structured representation of data,knowledge graph(KG) is capable of hosting domain-specific knowledge and modeling causal relationships among knowledge.However,most studies leverage KG to handle cybersecurity,while rarely pay attention to information security and production safety problems in industrial control systems.This paper proposes a set of construction methods for dual-security KG for process industrial control systems.Using the techniques of named entity recognition and relation extraction,it builds a large number of dual-security knowledge triples from a real-world production corpus.The built KG incorporates both features of chemical industry production process and potential network security flaws,providing comprehensive security guarantee for industrial control system.

Key words: Knowledge graph, Industrial control system, Dual security, Knowledge graph construction, Cybersecurity, Production safety

CLC Number: 

  • TP391
[1]CONTI M,DONADEL D,TURRIN F.A survey on industrialcontrol system testbeds and datasets for security research[J].IEEE Communications Surveys & Tutorials,2021,23(4):2248-2294.
[2]DING D,HAN Q L,XIANG Y,et al.A survey on security control and attack detection for industrial cyber-physical systems[J].Neurocomputing,2018,275:1674-1683.
[3]WOLF M,SERPANOS D.Safety and security in cyber-physical systems and internet-of-things systems[C]//Proceedings of the IEEE.2017:9-20.
[4]MAO S,ZHAO Y M,CHEN J H,et al.Development of process safety knowledge graph:a case study on delayed coking process[J].Computers & Chemical Engineering,2020,143:107094.
[5]CHEN Z Y,LIU Y,VALERA-MEDINA A,et al.Multi-sourced modelling for strip breakage using knowledge graph embeddings[J].Procedia CIRP,2021,104:1884-1889.
[6]LIANG H,PENG X J,ZHAO N,et al.An approach of top-down electric generation knowledge graph construction[J].IOP Conference Series:Earth and Environmental Science,2021,661(1):012021.
[7]WANG Z,ZHANG B,GAO D.A novel knowledge graph deve-lopment for industry design:A case study on indirect coal liquefaction process[J].Computers in Industry,2022,139:103647.
[8]EIBECK A,LIM M Q,KRAFT M.J-Park Simulator:anontology-based platform for cross-domain scenarios in process industry[J].Computers & Chemical Engineering,2019,131:106586.
[9]JIA Y,QI Y,SHANG H,et al.A practical approach to constructing a knowledge graph for cybersecurity[J].Engineering,2018,4(1):53-60.
[10]LI K,ZHOU H,TU Z,et al.CSKB:A Cyber Security Know-ledge Base Based on Knowledge Graph[C]//International Conference on Security and Privacy in Digital Economy.Singapore:Springer, 2020:100-113.
[11]RYEN V,SOYLU A,ROMAN D.Building semantic knowledge graphs from(semi-) structured data:a review[J].Future Internet,2022,14(5):129.
[12]HOGAN A,BLOMQVIST E,COCHEZ M,et al.Knowledgegraphs[J].ACM Computing Surveys(CSUR),2021,54(4):1-37.
[13]JI S,PAN S,CAMBRIA E,MARTTINEN P,et al.A survey on knowledge graphs:Representation,acquisition,and applications[J].IEEE Transactions on Neural Networks and Learning Systems,2021,33(2):494-514.
[14]EILICKE C,CHEKOL M W,RUFFINELLI D,et al.Anytimebot tom-up rule learning for knowledge graph completion[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.2019:3137-3143.
[15]PIPLAI A,MITTAL S,JOSHI A,et al.Creating cybersecurity knowledge graphs from malware after action reports[J].IEEE Access,2020,8:211691-211703.
[16]AL-MOSLMI T,OCAÑA M G,OPDAHL A L,et al.Named entity extraction for knowledge graphs:A literature overview[J].IEEE Access,2020,8:32862-32881.
[17]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.
[18]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems.2017:6000-6010.
[19]HUANG Z,XU W,YU K.Bidirectional LSTM-CRF models for sequence tagging[J].arXiv:1508.01991,2015.
[20]SOUZA F,NOGUEIRA R,LOTUFO R.Portuguese named entity recognition using BERT-CRF[J].arXiv:1909.10649,2019.
[21]MILAJERDI S M,ESHETE B,GJOMEMO R,et al.Poirot:Aligning attack behavior with kernel audit records for cyber threat hunting[C]//Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security.2019:1795-1812.
[22]NADEEM A,VERWER S,MOSKAL S,et al.Alert-driven attack graph generation using s-pdfa[J].IEEE Transactions on Dependable and Secure Computing,2021,19(2):731-746.
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