Overall Layout and Security Measures of Campus Wireless Local Area Networks

Duan, Yixue; Yang, Rui; Duan, Shaoyin

doi:10.1007/978-3-030-43306-2_4

Cited by 1 publication

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“…(1) Partitioning method: this kind of method is easy to describe and simple to implement, and it is also the most studied clustering method, the division method requires a given number of clusters k to be divided into, when classifying, it is first necessary to obtain a set of k initial divisions, then adopt the method of iterative relocation, and improve the quality of partitioning by moving data objects from one cluster to another [23]. The process can be represented as a binary hierarchical tree, the leaf node represents a data object, the middle point indicates that the dataset is split into two distinct classes, or a class is merged from its two subclasses.…”

Section: Classification Of Clustering Methodsmentioning

confidence: 99%

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

Liu

2022

Wireless Communications and Mobile Computing

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Attacks on network systems are becoming more and more common, the current state of increasingly sophisticated attack methods, the emergence of intrusion prevention technology is the inevitable result of the development of computer technology and network technology, and research on intrusion prevention has become a new focus of network security technology research in recent years. In order to ensure the security of computer network confidential information, the authors propose a semisupervised clustering intrusion detection algorithm. An overview of machine learning, followed by an explanation of the theory of cluster analysis, simulation experiments were carried out using the K -means algorithm and the semisupervised clustering algorithm proposed by the author, for 10,000 records, the K -means clustering algorithm and the semisupervised clustering algorithm described in this paper are used, respectively, and intrusion detection data tests were performed. At the same time, different K values were selected, three datasets were selected from “kddcup.newtestdata_10_percent_corrected,” the test data were tested separately, and their average value was taken as the test result. From the simulation results, the detection rate of the semisupervised clustering algorithm is higher than that of the K -means clustering algorithm, and the false alarm rate and K -means algorithms have also been improved. Therefore, the author’s semisupervised algorithm enhances the stability of the system, and the performance of the K -means algorithm is improved to a certain extent. When the value of K gradually increases, the false alarm rate also increases; however, when K is 20, the detection rate is maximized, from this, it can be known that when K is 20, its detection rate reaches 91.76%, and the false alarm rate is 8.54%. The detection rate of the author’s algorithm is significantly higher than the other two algorithms, the false positive rate is slightly higher than K -means, and the false positive rate is lower than that of the other algorithm, proving the superior performance of our algorithm.

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Section: Classification Of Clustering Methodsmentioning

confidence: 99%

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

Liu

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

Overall Layout and Security Measures of Campus Wireless Local Area Networks

Cited by 1 publication

References 5 publications

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

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