IOT security analysis of BDT-SVM multi-classification algorithm

被引：4
作者：
Li J. [1 ]
机构：
[1] College of International Eduacaion, Huanghuai University, Zhumadian
来源：
International Journal of Computers and Applications | 2023年 / 45卷 / 02期
关键词：
anonymous tree; BDT-SVM; Internet of Things; location privacy security; multi-classification algorithm;
D O I：
10.1080/1206212X.2020.1734313
中图分类号：
学科分类号：
摘要：
With the continuous development of the Internet of Things, the issue of IoT data security and privacy protection has received increasing attention. Compared with traditional Internet applications, IoT applications using smart terminals as supporting technologies have more complicated and serious security problems. This paper proposes a multi-classification algorithm based on double support vector machine decision tree. For all samples, the samples with the most separability were divided into two categories according to the size of the distinguishability between classes. In these two sub-categories, the most divisible samples are separately searched and divided into two categories, so that they can not be subdivided. This paper proposes a location privacy security protection mechanism based on anonymous tree and box structure, which provides location privacy protection for services oriented to intelligent terminals. The simulation results show that the common sub-collection of the anonymous group is larger, and the time overhead of building the group is smaller. At the same time, the BDT-SVM multi-classification algorithm can improve the accuracy of the intrusion detection system and reduce the detection time. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
引用
收藏
页码：170 / 179
页数：9
相关论文
共 21 条
  • [1] Hu C., Kong S., Wang R., Et al., Identification of migratory insects from their physical features using a decision-tree support vector machine and its application to radar entomology, Sci Rep, 8, 1, (2018)
  • [2] Wu Q., Zhong R., Zhao W., Et al., A comparison of pixel-based decision tree and object-based support vector machine methods for land-cover classification based on aerial images and airborne lidar data, Int J Remote Sens, 38, 23, pp. 7176-7195, (2017)
  • [3] Shu J., Liu S., Liu L., Et al., Research on link quality estimation mechanism for wireless sensor networks based on support vector machine∗, Chin J Electron, 26, 2, pp. 377-384, (2017)
  • [4] Yu O., Ishida H., Nakajima T.Y., Et al., Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2, Atmos Meas Tech, 11, 5, pp. 2863-2878, (2018)
  • [5] Sonmez R., S?zgen B., A support vector machine method for bid/no bid decision making, J CivEng Manag, 23, 5, pp. 641-649, (2017)
  • [6] Raczko E., Zagajewski B., Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images, Eur J Remote Sens, 50, 1, pp. 144-154, (2017)
  • [7] Gui G., Pan H., Lin Z., Et al., Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection, Ksce J Civ Eng, 21, 2, pp. 523-534, (2017)
  • [8] Maulik U., Chakraborty D., Remote sensing Image classification: a survey of support-vector-machine-based advanced techniques, IEEE Geosci Remote Sens Mag, 5, 1, pp. 33-52, (2017)
  • [9] Singh D., Roy D., Mohan C.K., DiP-SVM: distribution preserving kernel support vector machine for big data, IEEE T Big Data, 3, 1, pp. 79-90, (2017)
  • [10] Das S.P., Padhy S., A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting, Int J Mach Learn Cybern, 9, 1, pp. 97-111, (2018)
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