Abstract
With the development of IT technology, cyber intelligence, surveillance, and reconnaissance (ISR) have become more important than traditional military ISR. In this paper, we propose an effective method to efficiently operate cyber ISR using machine learning, especially utilizing incremental learning methods. We compare two learning methods that are suitable for use in the self-learning agent model of self-survival in a closed network. The result shows that the incremental learning method reduces the memory needed by more than 20%, with an accuracy of more than 82%. Thus, the incremental learning method can be very effective in a closed military network.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ade R, Deshmukh P (2013) Methods for incremental learning: a survey. Int J Data Min Knowl Manag Process 3(4):119–125. https://doi.org/10.5121/ijdkp.2013.3408
Bifet A, Holmes G, Pfahringer B, Kirkby R, Gavaldà R (2009) New Ensemble Methods For Evolving Data Streams. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Paris, (pp 139–148). https://doi.org/10.1145/1557019.1557041
Chantrapornchai C, Nusawat P (2016) Two machine learning models for mobile phone battery discharge rate prediction based on usage patterns. J Inf Process Syst 12(3):436–454. https://doi.org/10.3745/JIPS.03.0048
Chizek J, Elsea J, Best R, Bolkcom C (2003) Military Transformation: Intelligence, Surveillance and Reconnaissance. Nova Science Publishers, New york
Domingos P Hulten G (2000) Mining high-speed data streams. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Boston, pp 71–80. https://doi.org/10.1145/347090.347107
Eom J, Kim N, Kim S, Chung T (2012) Cyber Military Strategy for Cyberspace Superiority in Cyber warfare. Cyber Security, In: Proceedings of the 2012 international conference on cyber warfare and digital forensic (CyberSec), IEEE, Kuala Lumpur, pp 295–299. https://doi.org/10.1109/cybersec.2012.6246114
Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning (Icml), Bari, pp 148–156
Gama J, Kosina P (2011) Learning decision rules from data streams. In: Proceedings of the twenty-second international joint conference on artificial intelligence, Barcelona, pp 1255–1260
Gijsberts A, Metta G (2011) Incremental learning of robot dynamics using random features. In: Proceedings of the 2011 IEEE international conference on robotics and automation, IEEE, Shanghai, pp 951–956. https://doi.org/10.1109/icra.2011.598.0191
Hasan M, Roy-Chowdhury A (2016) Incremental learning of human activity models from videos. Comput Vis Image Underst 144:24–35. https://doi.org/10.1016/j.cviu.2015.10.018
Hoeglinger S, Pears R (2007) Use of hoeffding trees in concept based data stream mining. In: Proceedings of the 2007 third international conference on information & automation for sustainbility, IEEE, Melbourne, pp 57–62. https://doi.org/10.1109/iciafs.2007.4544780
Hurley M (2012) For and from Cyberspace:Conceptualizing Cyber Intelligence, Surveillance, and Reconnaissance. AIR UNIV MAXWELL AFB AL AIR FORCE RESEARCH INST
Kalmegh S (2015) Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News. Int J Innov Sci Eng Technol 2(2):438–446
Kayacik H, Zincir-Heywood A, Heywood MI (2005) Selecting features for intrusion detection: a feature relevance analysis on KDD 99 intrusion detection datasets. In: Proceedings of the third annual conference on privacy, security and trust(PST), New Brunswick
Kim S, Lee H, Kwon H, Lee S (2015) Evaluation model of defense information systems use. J Converg (JoC) 6(1):18–26
Le T, Stahl F, Gomes J, Gaber M, Fatta G (2014) Computationally efficient rule-based classification for continuous streaming data. In: Proceedings of the international conference on innovative techniques and applications of artificial intelligence, Springer, Cambridge, pp 21–34. https://doi.org/10.1007/978-3-319-12069-0_2
Nurelmadina N, Nafea I, Younas M (2016) Evaluation of a channel assignment scheme in mobile network systems. Hum Centric Comput Inf Sci 6(1):21. https://doi.org/10.1186/s13673-016-0075-0
Olusola A, Oladele A, Abosede D (2010) Analysis of KDD’99 intrusion detection dataset for selection of relevance features. In: Proceedings of the World Congress on Engineering and Computer Science(WCECS), San Francisco, pp 20–22
Polikar R et al (2001) Learn ++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybern Part C 31(4):497–508. https://doi.org/10.1109/5326.983933
Read J, Bifet A, Pfahringer B, Holmes G (2012) Batch-incremental versus instance-incremental learning in dynamic and evolving data. In: Proceedings of the international symposium on intelligent data analysis, Springer, Helsinki, pp 313–323. https://doi.org/10.1007/978-3-642-34156-4_29
Tama B (2015) Learning to Prevent Inactive Student of Indonesia Open University. J Inf Process Syst 11(2):165–172. https://doi.org/10.3745/JIPS.04.0015
Wang Y, Fan X, Luo Z (2017) Fast online incremental learning on mixture streaming data. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, San Francisco, pp 2739–2745
Wu J, Zhang G, YaZhou R, XiaYan Z, Qiao Y (2017) Weighted local naive bayes link prediction. J Inf Process Syst 13(4):914–927. https://doi.org/10.3745/JIPS.04.0040
Xiao R, Wang J, Zhang F (2000) An approach to incremental SVM learning algorithm. In: Proceedings of the 12th IEEE internationals conference on tools with artificial intelligence 2000, IEEE, Vancouver, pp 268–273. https://doi.org/10.1109/tai.2000.889881
Xie T, Peng Y, Wang C (2016) hi-RF: Incremental learning random forest for large-scale multi-class data classification. In: Proceedings of the 8th asian conference on machine learning, Hamilton, pp 1–17
Yang H, Fong S, Sun G, Wong R (2012) A very fast decision tree algorithm for real-time data mining of imperfect data streams in a distributed wireless sensor network. Int J Distrib Sens Netw. https://doi.org/10.1155/2012/863545
Zouina M, Outtaj B (2017) A novel lightweight URL phishing detection system using SVM and similarity index. Hum Centric Comput Inf Sci. https://doi.org/10.1186/s13673-017-0098-1
Acknowledgements
This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract(UD160066BD).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by G. Yi.
Rights and permissions
About this article
Cite this article
Shin, G., Yooun, H., Shin, D. et al. Incremental learning method for cyber intelligence, surveillance, and reconnaissance in closed military network using converged IT techniques. Soft Comput 22, 6835–6844 (2018). https://doi.org/10.1007/s00500-018-3433-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-018-3433-1