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Entropy-Based Anomaly Detection Using Observation Points Relations in Wireless Sensor Networks

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Abstract

Wireless sensor networks play the most important role in the internet of things (IoT). Due to the extensive development of sensor networks, the innate limitations and characteristics of the resources in sensors, and heterogeneity of equipment, wireless sensor network has been confronted with security challenges and various vulnerabilities. One way to improve the reliability of the sensor networks is to use abnormal behaviors detection methods in the network. The current paper presents a detection method for abnormal behaviors in a distributed way using a division technique based on entropy and closeness of cumulative observation points. The proposed algorithm for detecting intrusions processed in an intranetwork manner and separates abnormal data from normal data and then classifies it and reports to the higher levels. To investigate the efficiency and accuracy of the detection, multilayer hierarchical topology and simulations based on MATLAB software were employed. The results show high accuracy of the proposed method in detecting abnormal behavior in different levels of wireless sensor networks.

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References

  1. Shahid, N., Naqvi, I. H., & Qaisar, S. B. (2015). Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: A survey. Artificial Intelligence Review, 43(2), 193–228.

    Article  Google Scholar 

  2. Kumarage, H., Khalil, I., Tari, Z., & Zomaya, A. (2013). Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling. Journal of Parallel and Distributed Computing, 73(6), 790–806.

    Article  Google Scholar 

  3. Sherasiya, T., Upadhyay, H., & Patel, H. B. (2016). A survey: Intrusion detection system for internet of things. International Journal of Computer Science and Engineering (IJCSE), 5, 2.

    Google Scholar 

  4. Forkan, A. R. M., Khalil, I., & Atiquzzaman, M. (2017). ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data. Computer Networks, 113, 244–257.

    Article  Google Scholar 

  5. Li, B., Lu, R., Wang, W., & Choo, K. K. R. (2017). Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. Journal of Parallel and Distributed Computing, 103, 32–41.

    Article  Google Scholar 

  6. Kumarage, H., Khalil, I., Alabdulatif, A., Tari, Z., & Yi, X. (2016). Secure data analytics for cloud-integrated internet of things applications. IEEE Cloud Computing, 2, 46–56.

    Article  Google Scholar 

  7. Qiu, T., Chen, N., Li, K., Atiquzzaman, M., & Zhao, W. (2018). How can heterogeneous internet of things build our future: A survey. IEEE Communications Surveys & Tutorials, 20, 2011–2027.

    Article  Google Scholar 

  8. Park, T., Cho, D., & Kim, H. (2018). An effective classification for DoS attacks in wireless sensor networks. In 2018 Tenth international conference on ubiquitous and future networks (ICUFN) (pp. 689–692). IEEE.

  9. Moshtaghi, M., Leckie, C., & Karunasekera, S. (2016). A framework for distributed data analysis for IoT. In Internet of things (pp. 163–180).

  10. Forkan, A. R. M., Khalil, I., & Atiquzzaman, M. (2017). ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data. Computer Networks, 113, 244–257.

    Article  Google Scholar 

  11. Yao, H., Fu, X., Yang, Y., & Postolache, O. (2018). An incremental local outlier detection method in the data stream. Applied Sciences, 8(8), 1248.

    Article  Google Scholar 

  12. Bosman, H. H., Iacca, G., Tejada, A., WÖrtche, H. J., & Liotta, A. (2017). Spatial anomaly detection in sensor networks using neighborhood information. Information Fusion, 33, 41–56.

    Article  Google Scholar 

  13. Curiac, D., & Volosencu, C. (2012). Ensemble based sensing anomaly detection in wireless sensor networks. Expert Systems with Applications, 39(10), 9087–9096.

    Article  Google Scholar 

  14. Serdio, F., Lughofer, E., Pichler, K., Buchegger, T., Pichler, M., & Efendic, H. (2014). Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations. Information Fusion, 20, 272–291.

    Article  Google Scholar 

  15. Gaillard, F., Autret, E., Thierry, V., Galaup, P., Coatanoan, C., & Loubrieu, T. (2009). Quality control of large Argo datasets. Journal of Atmospheric and Oceanic Technology, 26(2), 337–351.

    Article  Google Scholar 

  16. Giatrakos, N., Deligiannakis, A., Garofalakis, M., & Kotidis, Y. (2018). Omnibus outlier detection in sensor networks using windowed locality sensitive hashing. Future Generation Computer Systems.

  17. Suthaharan, S., Leckie, C., Moshtaghi, M., Karunasekera, S., & Rajasegarar, S. (2010). Sensor data boundary estimation for anomaly detection in wireless sensor networks. In 7th International conference in mobile adhoc and sensor systems (MASS) (pp. 546–551). IEEE.

  18. Kumarage, H., Khalil, I., & Tari, Z. (2015). Granular evaluation of anomalies in wireless sensor networks using dynamic data partitioning with an entropy criteria. IEEE Transactions on Computers, 64(9), 2573–2585.

    Article  MathSciNet  Google Scholar 

  19. Giatrakos, N., et al. (2018). Omnibus outlier detection in sensor networks using windowed locality sensitive hashing. Future Generation Computer Systems.

  20. Forkan, A. R. M., Khalil, I., & Atiquzzaman, M. (2017). ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data. Computer Networks, 113, 244–257.

    Article  Google Scholar 

  21. Ahmad, B., Jian, W., Ali, Z. A., Tanvir, S., & Khan, M. S. A. (2019). Hybrid anomaly detection by using clustering for wireless sensor network. Wireless Personal Communications, 106(4), 1841–1853.

    Article  Google Scholar 

  22. de Souza, P. S. S., Rubin, F. P., Hohemberger, R., Ferreto, T. C., Lorenzon, A. F., Luizelli, M. C., & Rossi, F. D. (2020). Detecting abnormal sensors via machine learning: An IoT farming WSN-based architecture case study. Measurement, 164, 108042.

    Article  Google Scholar 

  23. Poornima, I. G. A., & Paramasivan, B. (2020). Anomaly detection in wireless sensor network using machine learning algorithm. Computer Communications, 151, 331–337.

    Article  Google Scholar 

  24. Chen, Y., & Li, S. (2019). A lightweight anomaly detection method based on SVDD for wireless sensor networks. Wireless Personal Communications, 105(4), 1235–1256.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Engineering and Information Technology Department, Razi University, Kermanshah, Iran

    Ahmad Shahab Arkan & Mahmood Ahmadi

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  1. Ahmad Shahab Arkan
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  2. Mahmood Ahmadi
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Correspondence to Mahmood Ahmadi.

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Arkan, A.S., Ahmadi, M. Entropy-Based Anomaly Detection Using Observation Points Relations in Wireless Sensor Networks. Wireless Pers Commun 119, 1783–1798 (2021). https://doi.org/10.1007/s11277-021-08306-5

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