
A Practical Approach to Constructing a Knowledge Graph for Cybersecurity
Yan Jia, Yulu Qi, Huaijun Shang, Rong Jiang, Aiping Li
Engineering ›› 2018, Vol. 4 ›› Issue (1) : 53-60.
A Practical Approach to Constructing a Knowledge Graph for Cybersecurity
Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER) is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work.
Cybersecurity / Knowledge graph / Knowledge deduction
[1] |
|
[2] |
|
[3] |
J. Undercoffer, A. Joshi, J. Pinkston. Modeling computer attacks: An ontology for intrusion detection. G. Vigna, E. Jonsson, C. Kruegel (Eds.), RAID 2003: Recent advances in intrusion detection, 2003 Sep 8-10; Pittsburgh, PA, USA, Springer, Berlin (2003), pp. 113-135. DOI: 10.1007/978-3-540-45248-5_7
|
[4] |
A. Joshi, R. Lal, T. Finin, A. Joshi. Extracting cybersecurity related linked data from text. Proceedings of the 7th IEEE international conference on semantic computing, 2013 Sep 16-18; Irvine, CA, USA, IEEE Computer Society Press, Los Alamitos (2013), pp. 252-259. DOI: 10.1109/ICSC.2013.50
|
[5] |
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
R. Lal. Information extraction of cybersecurity related terms and concepts from unstructured text dissertation. University of Maryland, College Park (2013)
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
J.R. Finkel, T. Grenager, C. Manning. Incorporating non-local information into information extraction systems by Gibbs sampling. K. Knight, H.T. Ng, K. Oflazer (Eds.), Proceedings of the 43rd annual meeting of the association for computational linguistics, 2005 Jun 25-30; Ann Arbor, MI, USA, Association for Computational Linguistics, Stroudsburg (2005), pp. 363-370. DOI: 10.3115/1219840.1219885
|
[28] |
|
/
〈 |
|
〉 |