《工程(英文)》 >> 2018年 第4卷 第1期 doi: 10.1016/j.eng.2018.01.004
一种构建网络安全知识图谱的实用方法
School of Computer Science, National University of Defense Technology, Changsha 410073, China
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[ 1 ] Zhu J, Zhang J, Zhang C, Wu Q, Jia Y, Zhou B, et al. CHRS: Cold start recommendation across multiple heterogeneous information networks. IEEE Access 2017;5:15283–99. 链接1
[ 2 ] Zhu X, Huang J, Zhou B, Li A, Jia Y. Real-time personalized twitter search based on semantic expansion and quality model. Neurocomputing 2017;254:13–21. 链接1
[ 3 ] Undercoffer J, Joshi A, Pinkston J. Modeling computer attacks: An ontology for intrusion detection. In: Vigna G, Jonsson E, Kruegel C, editors. RAID 2003: 链接1
[ 4 ] Joshi A, Lal R, Finin T, Joshi A. Extracting cybersecurity related linked data from text. In: Proceedings of the 7th IEEE international conference on semantic computing. Los Alamitos: IEEE Computer Society Press; 2013. p. 252–9. 链接1
[ 5 ] More S, Matthews M, Joshi A, Finin T. A knowledge-based approach to intrusion detection modeling. In: Proceedings of 2012 IEEE symposium on security and privacy workshops. Los Alamitos: IEEE Computer Society Press; 2012. p. 75–81. 链接1
[ 6 ] Obrst L, Chase P, Markeloff R. Developing an ontology of the cybersecurity domain. CEUR Workshop Proc 2012;966:49–56.
[ 7 ] Parmelee MC. Toward an ontology architecture for cyber-security standards. CEUR Workshop Proc 2010;713:116–23. 链接1
[ 8 ] Iannacone M, Bohn S, Nakamura G, Gerth J, Huffer K, Bridges R, et al. Developing an ontology for cybersecurity knowledge graphs. In: Proceedings of the 10th annual cyber and information security research conference. New York: ACM, Inc.; 2015. 链接1
[ 9 ] Pinkston J, Undercoffer J, Joshi A, Finin T. A target-centric ontology for intrusion detection. In: Proceedings of the IJCAI-03 workshop on ontologies and distributed systems, Aug 9–15, 2003, Acapulco, Mexico; 2003. p. 47–58. 链接1
[10] Rehman S, Mustafa K. Software design level vulnerability classification model. Int J Comput Sci Secur 2012;6(4):238–55. 链接1
[11] Lowis L, Accorsi R. On a classification approach for SOA vulnerabilities. In: Proceedings of the 33rd annual IEEE international computer software and applications conference. Los Alamitos: IEEE Computer Society Press; 2009. p. 439–44. 链接1
[12] Lal R. Information extraction of cybersecurity related terms and concepts from unstructured text [dissertation]. College Park: University of Maryland; 2013.
[13] Mulwad V, Li W, Joshi A, Finin T, Viswanathan K. Extracting information about security vulnerabilities from web text. In: Hübner JF, Petit JM, Suzuki E, editors. Proceedings of 2011 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology—workshops. Los Alamitos: IEEE Computer Society Press; 2011. p. 257–60. 链接1
[14] CNNVD.org.cn [Internet]. Beijing: China Information Technology Security Evaluation Center; [cited 2017 Jul 25]. Available from: http://www.cnnvd. org.cn/. Chinese.
[15] NVD.nist.gov [Internet]. Gaithersburg: National Institute of Standards and Technology; [cited 2017 Jul 25]. Available from: https://nvd.nist.gov/.
[16] Paulheim H, Bizer C. Type inference on noisy RDF data. In: Alani H, Kagal L, Fokoue A, Groth P, Biemann C, Parreira JX, et al., editors. The semantic web— ISWC 2013: Proceedings of the 12th international semantic web conference. Berlin: Springer; 2013. p. 510–25. 链接1
[17] Paulheim H, Bizer C. Type inference on noisy RDF data. In: Cudré-Mauroux P, Heflin J, Sirin E, Tudorache T, Euzenat J, Hauswirth M, et al., editors. The semantic web—ISWC 2012: Proceedings of the 11th international semantic web conference. Berlin: Springer; 2012. p. 65–81. 链接1
[18] Kliegr T. Linked hypernyms: Enriching DBpedia with targeted hypernym discovery. J Web Semant 2015;31:59–69. 链接1
[19] Lehmann J, Auer S, Bühmann L, Tramp S. Class expression learning for ontology engineering. J Web Semant 2011;9(1):71–81. 链接1
[20] Hellmann S, Lehmann J, Auer S. Learning of OWL class descriptions on very large knowledge bases. Int J Semant Web Inf Syst 2009;5(2):25–48. 链接1
[21] Lehmann J. DL-learner: Learning concepts in description logics. J Mach Learn Res 2009;10(11):2639–42. 链接1
[22] Völker J, Niepert M. Statistical schema induction. In: Antoniou G, Grobelnik M, Simperl E, Parsia B, Plexousakis D, De Leenheer P, et al., editors. The semantic web: Research and applications: Proceedings of the 8th extended semantic web conference. Berlin: Springer; 2011. p. 124–38. 链接1
[23] Fleischhacker D, Völker J. Inductive learning of disjointness axioms. In: Meersman R, Dillon T, Herrero P, Kumar A, Reichert M, Qing L, et al., editors. On the move to meaningful internet systems: OTM 2011: Proceedings of confederated international conferences: CoopIS, DOA-SVI, and ODBASE 2011. Berlin: Springer; 2011. p. 680–97. 链接1
[24] Völker J, Fleischhacker D, Stuckenschmidt H. Automatic acquisition of class disjointness. J Web Semant 2015;35(Pt 2):124–39. 链接1
[25] Singhal A. Introducing the knowledge graph: Things, not strings [Internet].[updated 2012 May 16; cited 2017 Jul 25]. Available from: https://googleblog. blogspot.com/2012/05/introducing-knowledge-graphthings-not.html.
[26] Lin D, Wu X. Phrase clustering for discriminative learning. In: Proceedings of the 47th annual meeting of the association for computational linguistics and the 4th international joint conference on natural language processing of the AFNLP. Singapore: Suntec; 2009. p. 1030–8. 链接1
[27] Finkel JR, Grenager T, Manning C. Incorporating non-local information into information extraction systems by Gibbs sampling. In: Knight K, Ng HT, Oflazer K, editors. Proceedings of the 43rd annual meeting of the association for computational linguistics. Stroudsburg: Association for Computational Linguistics; 2005. p. 363–70.
[28] NERFeatureFactory [Internet]. Stanford: Stanford NLP Group; [updated 2013 Jun 26; cited 2017 Jul 25]. Available from: http://nlp.stanford.edu/ nlp/javadoc/javanlp/edu/stanford/nlp/ie/NERFeatureFactory.html.