Abstract
The impact of the Internet on the power industry is increasing, the detection of power network vulnerability becomes more and more important. Traditional power network vulnerabilities detection methods are relatively labor-intensive and inefficient, so, the power network vulnerability detection algorithm based on improved Adaboost is proposed in this paper. It is a kind of machine learning algorithm, which select C4.5 decision tree as weak classifier to integrate a strong classifier. Compared with neural network, KNN and other methods, the proposed algorithm is more efficient in power network vulnerability detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Y., Che, W., Liu, T., Zhang, M.: A comparison study of sequence labeling methods for Chinese word segmentation, POS tagging models. J. Chin. Inf. Process. 27, 30–36 (2013)
Feng, X.Z., Hao, P.: Information of product review mining based on analyzing of part of speech. Comput. Eng. Des. 34(1), 283–288 (2013)
Juan, Y.U., Dang, Y.Z.: Chinese term extraction based on POS analysis & string frequency. Syst. Eng.-Theory Pract. (2010)
Wei, Y.G., Zhang, G.C., Chang, Y., Yuan, F.: Deep web semantic annotation method based on Chinese part-of-speech and domain knowledge. J. Zhengzhou Univ. (2009)
Ouerdi, N., Elfarissi, I., Azizi, A., Azizi, M., et al.: Artificial neural network-based methodology for vulnerabilities detection in EMV cards. In: International Conference on Information Assurance and Security, pp. 85–90. IEEE (2015)
Tarik, H., Ouerdi, N.: EMV cards vulnerabilities detection using ANN. In: International Conference on Information Technology for Organizations Development (2016)
Chow, M.Y., Sharpe, R.N., Hung, J.C.: On the application and design of artificial neural networks for motor fault detection. IEEE Trans. Ind. Electron. 40(2), 189–196 (1993)
Tzafestas, S.G., Dalianis, P.J.: Fault diagnosis in complex systems using artificial neural networks. In: Proceedings of the Third IEEE Conference on Control Applications, pp. 877–882. IEEE Xplore, Glasgow (1994)
Wang, H.: Actuator fault diagnosis for nonlinear dynamic system. Trans. Inst. Meas. Control. 17(2), 63–71 (1995)
Polycarpou, M.M., Helmicki, A.J.: Automated fault detection and accommodation: a learning systems approach. IEEE Trans. Syst. Man Cybern. 25(11), 1447–1458 (1995)
Patton, R.J., Chen, J., Siew, T.M.: Fault diagnosis in nonlinear dynamic systems via neural networks. In: International Conference on Control, IET, vol. 2, pp. 1346–1351 (2002)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. Mol. Biol. 147, 195–197 (1981)
Valiant, L.G.: A theory of the learnable. Commun. ACM 27(11), 1134–1142 (1984)
Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59119-2_166
Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Press 13(1), 21–27 (1967)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., Burlington (1993)
UCI repository of machine learning databases. http://www.ics.uci.edu
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Tao, W., Liu, S., Su, Y., Hu, C. (2018). Power Network Vulnerability Detection Based on Improved Adaboost Algorithm. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11065. Springer, Cham. https://doi.org/10.1007/978-3-030-00012-7_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-00012-7_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00011-0
Online ISBN: 978-3-030-00012-7
eBook Packages: Computer ScienceComputer Science (R0)