[1]辛壮,万良,李均涛.改进的聚类算法在网络异常行为检测中的应用[J].计算机技术与发展,2019,29(03):111-116.[doi:10.3969/ j. issn.1673-629X.2019.03.024]
　XIN Zhuang,WAN Liang,LI Jun-tao.Application of Improved Clustering Algorithm in Network Abnormal Behavior Detection[J].,2019,29(03):111-116.[doi:10.3969/ j. issn.1673-629X.2019.03.024]
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改进的聚类算法在网络异常行为检测中的应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年03期
页码:
111-116
栏目:
安全与防范
出版日期:
2019-03-10

文章信息/Info

Title:
Application of Improved Clustering Algorithm in Network Abnormal Behavior Detection
文章编号:
1673-629X(2019)03-0111-06
作者:
辛壮1; 万良1; 李均涛2
1. 贵州大学 计算机科学与技术学院,贵州 贵阳 550025;2. 贵州财经大学 信息学院,贵州 贵阳 550025
Author(s):
XIN Zhuang1; WAN Liang1; LI Jun-tao2
1. School of Computer Science and Technology,Guizhou University,Guiyang 550025,China;2. School of Information,Guizhou University of Finance and Economics,Guiyang 550025,China
关键词:
K-means; 最小生成树; 网络异常行为; 聚类; 数据挖掘
Keywords:
K-means; minimum spanning tree; network anomalous behavior; clustering; data mining
分类号:
TP39
DOI:
10.3969/ j. issn.1673-629X.2019.03.024
摘要:
网络异常行为检测是对大规模网络数据流量进行分析并发现入侵行为的一种方法。 针对基于聚类的网络异常行为检测方法不能及时准确地选择初始聚类中心、无法有效地识别非球状簇等问题,提出一种改进的聚类算法应用在网络异常行为检测中。 该方法使用最小生成树算法获得初始聚类中心,使用改进的 K-means 聚类算法区分异常行为与正常行为,通过距离比值判断聚类效果,提高了聚类效果的准确性。 通过应用有监督学习的方式对聚类结果进行检测,结果表明,改进的聚类算法能够更好地识别初始聚类中心,并进行更加有效的聚类,能够更加准确地检测出网络异常行为。
Abstract:
Network abnormal behavior detection is a method to analyze and discover the intrusion behavior of large-scale network data flow. The anomalous behavior detection method based on clustering cannot timely and correctly select the initial clustering center,and is unable to effectively identify the globular clusters. In order to solve these problems,we propose an improved clustering algorithm in the network abnormal behavior detection. This method obtains the initial clustering center by using the minimum spanning tree algorithm, distinguishing the abnormal behavior from the normal behavior by the improved K-means clustering algorithm and judging the clusteringeffect by the distance ratio,which improves the accuracy of the clustering effect. The results tested by supervised learning show that theimproved clustering algorithm can better identify the initial clustering center and make more effective clustering,and detect the network anomaly behavior more accurately.

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更新日期/Last Update: 2019-03-10