Abstract
In recent years use of a Wireless Sensor Network (WSN) has become a leading area of research in various applications. Because of WSN limitation of its own features that low ability of calculation, small volume of storage, resource constrain, bad communicational environment of wireless, which lead to WSN be besieged by imminent security problems, the abnormal detection in WSN is vary important at this time. We focus on the quality of data, and aim at the characteristics of wireless sensor network node data with time and spatial similarity, and the current research on anomaly analysis. We use the classification to detect outliers. This paper presents a method of detecting the proximity of distance based on distance, which is the main reason for the study of the anomalous value of the network. The KNN (K-Nearest Neighbor) algorithm is used to analyze and detect the data to achieve the purpose of data anomaly detection in WSN. The algorithm of outlier detection is based on the design and achieve of QualNet simulation platform. It is effective and accurate to evaluate and test. The simulation results show the effectiveness of the proposed KNN algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Branch, J., Szymanski, B., Giannella, C., et al.: In-network outlier detection in wireless sensor networks. In: Proceedings of the IEEE ICDCS, pp. 384–391 (2006)
Madeti, S.R., Singh, S.N.: Modeling of PV system based on experimental data for fault detection using kNN method. J. Solar Energy 173, 139–151 (2018)
Baragona, R., Battaglia, F., Cucina, D.: Empirical likelihood for outlier detection and estimation in autoregressive time series. J. Time 37, 315–336 (2015)
Zhang, Y., Meratnia, N., Havinga, P.J.M.: Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine. Ad Hoc Netw. 11, 1062–1074 (2013)
Cheng, J., Zhou, J., Liu, Q., Tang, X., Guo, Y.: A DDoS detection method for socially aware networking based on forecasting fusion feature sequence. Comput. J. 61, 959–970 (2018). https://doi.org/10.1093/comjnl/bxy025
Zhang, X., Liu, Y., Luo, B., Pan, L.: Computational power of tissue P systems for generating control languages. Inf. Sci. 278, 285–297 (2014)
Cheng, J., Xu, R., Tang, X., Sheng, V.S., Cai, C.: An abnormal network flow feature sequence prediction approach for DDoS attacks detection in big data environment. Comput. Mater. Continua 55(1), 095–119 (2018)
Zhang, G., Li, F.: Application of the KNN algorithm based on KD tree in intelligent transportation system. In: 2014 IEEE 5th International Conference on Software Engineering and Service Science (2014)
Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980)
Guo, Y.: Centipeda minima (Ebushicao) extract inhibits PI3K-Akt-mTOR signaling in nasopharyngeal carcinoma CNE-1 cells. Chin. Med. 10, 26 (2015)
Hu, X., Zhu, F.: Metadata web classifiers using KNN and porter. In: 2013 International Conference on Education and Educational Research (2013)
Wang, Q., Wang, S., Meng, Z.: Applying an intrusion detection algorithm to wireless sensor networks. In: Second International Workshop on Knowledge Discovery and Data Mining. IEEE Computer Society (2009)
He, Z., Zhu, H., Yu, F.: A vehicle detection algorithm based on wireless magnetic sensor networks. In: 2014 4th IEEE International Conference on Information Science and Technology (2014)
Wei, Z., Zhigang, L.: Network modeling and simulation based on QualNet. In: The 15th Information Theory Academic Annual Meeting of the Chinese Institute of Electronics and the 1st National Network Coding Academic Annual Meeting. National Defense Industry Press (2008)
Balasundaram, A., Rajesh, L., Bagan, K.B.: A study on fourth generation wireless network using QualNet simulator. In: SCIEI 2015 Paris Conference.th. Madras Institute of Technology, Anna University, Paris (2015)
Liu, M., Qiao, H., Lan, J.: Research on communication network simulation based on QualNet. Electron. Des. Eng. (10), 33–136 (2014)
Xu, C., Ni, S.Z., Shao, H.G.: Energy balanced self-adaptive intelligent water drops routing algorithm. In: Proceedings of the 2014 International Conference on Future Information Engineering and Manufacturing Science (2014)
Acknowledgment
This work is supported by the Key Research Project of Hainan Province [ZDYF2018129], and by the Natural Science Foundation of China [61762033] and the Natural Science Foundation of Hainan [20166227,617048, 2018CXTD333], the Key Innovation and Entrepreneurship Project of Hainan University [Hdcxcyxm201711].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, L., Li, J., Bhatti, U.A., Liu, Y. (2019). Anomaly Detection in Wireless Sensor Networks Based on KNN. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11634. Springer, Cham. https://doi.org/10.1007/978-3-030-24271-8_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-24271-8_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24270-1
Online ISBN: 978-3-030-24271-8
eBook Packages: Computer ScienceComputer Science (R0)