Abstract
An intrusion detection method which is suitable for the characteristics of WSN (wireless sensor networks) is proposed intrusion detection based on single-class support vector machine. SVM (Support vector machines) can directly train and model the collected data sets, automatically generate detection models, and improve the efficiency of intrusion detection systems. A three-layer intrusion detection model is defined based on this algorithm. The model is more effectively for classifying the data collected by cluster member nodes into intrusion data and normal data. Finally, On the QualNet simulation platform, we implement SVM for the detection of DoS (denial of service) attacks intrusion detection algorithm. The result show that it is feasible to apply SVM to the design of intrusion detection system, with higher system detection rate and lower false alarm rate.
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Acknowledgement
This work is supported by the Key Research Project of Hainan Province [ZDYF2018129], and by the National Natural Science Foundation of China [61762033] and The National Natural Science Foundation of Hainan [617048, 2018CXTD333].
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Wang, L., Li, J., Cheng, J., Bhatti, U.A., Dai, Q. (2018). DoS Attacks Intrusion Detection Algorithm Based on Support Vector Machine. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_26
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DOI: https://doi.org/10.1007/978-3-030-00018-9_26
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