基于Squeezer算法与Java技术的WSN入侵检测系统
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摘要
无线传感器网络(WSN)是一种基于数据为中心,通过自组织的方式构成的网络,同时用节点中的传感器测量所在外部环境的各种信号,从中探测出很多异常现象,可以实现对所在环境的监测。
WSN中的数据是以数据流(Data Streams)的形式出现,如何从大量动态变化的数据中快速获取有用信息具有非常重要的意义。
数据流挖掘是数据挖掘的一个新的研究方向,离群点检测是数据流挖掘重要内容。而基于数据流离群点检测的无线传感器网络异常入侵检测研究也受到了网络安全学界的关注,国内外有些会议或论文关注到了其相关应用研究,但还没有具体的相关产品。
Squeezer算法是一种适合在数据流上检测出离群点的算法,可以快速地在数据流中检测出异常数据,同样也适合于无线传感器中的检测出离群点。
本文结合了Java开发技术和数据流挖掘技术,设计了无线传感器环境下的入侵检测系统模型,并进行了相关需求分析,系统模型的设计,以及系统原型的生成及应用,测试结果证明该系统有好的检测效果,有较高的应用价值。
Wireless sensor network is based on the data center, by way of self-organization a network, while nodes in the sensor with the external environment in which the various signals detected from a lot of anomalies, which can be achieved on the monitoring of the environment.
The data in wireless sensor networks based on the form of data streams, how to dynamically changing data from a large number of useful information quickly is very important significance.
Data stream mining is a new research of data mining, outlier detection is an important part of data stream mining. The displaced groups based on the data detection for wireless sensor network anomaly intrusion detection has also been the concern of the academic network security, domestic and international concern about some meetings or papers related to the application of their research, but no concrete related products.
Squeezer algorithm is a data stream suitable for outlier detection algorithm to quickly detect in the data stream to achieve abnormal, also suitable for wireless sensors to detect outliers.
In this paper, a Java development technology and data stream mining technology, design a wireless sensor environment, the intrusion detection system model, and the related requirements analysis, system model design, and the generation and application of the system prototype, test results show that the system Good test results, a higher value.
WSN中的数据是以数据流(Data Streams)的形式出现,如何从大量动态变化的数据中快速获取有用信息具有非常重要的意义。
数据流挖掘是数据挖掘的一个新的研究方向,离群点检测是数据流挖掘重要内容。而基于数据流离群点检测的无线传感器网络异常入侵检测研究也受到了网络安全学界的关注,国内外有些会议或论文关注到了其相关应用研究,但还没有具体的相关产品。
Squeezer算法是一种适合在数据流上检测出离群点的算法,可以快速地在数据流中检测出异常数据,同样也适合于无线传感器中的检测出离群点。
本文结合了Java开发技术和数据流挖掘技术,设计了无线传感器环境下的入侵检测系统模型,并进行了相关需求分析,系统模型的设计,以及系统原型的生成及应用,测试结果证明该系统有好的检测效果,有较高的应用价值。
Wireless sensor network is based on the data center, by way of self-organization a network, while nodes in the sensor with the external environment in which the various signals detected from a lot of anomalies, which can be achieved on the monitoring of the environment.
The data in wireless sensor networks based on the form of data streams, how to dynamically changing data from a large number of useful information quickly is very important significance.
Data stream mining is a new research of data mining, outlier detection is an important part of data stream mining. The displaced groups based on the data detection for wireless sensor network anomaly intrusion detection has also been the concern of the academic network security, domestic and international concern about some meetings or papers related to the application of their research, but no concrete related products.
Squeezer algorithm is a data stream suitable for outlier detection algorithm to quickly detect in the data stream to achieve abnormal, also suitable for wireless sensors to detect outliers.
In this paper, a Java development technology and data stream mining technology, design a wireless sensor environment, the intrusion detection system model, and the related requirements analysis, system model design, and the generation and application of the system prototype, test results show that the system Good test results, a higher value.
引文
[1]马祖长,孙怡宁,梅涛.无线传感器网络综述.通信学报.2004,25(4):114-124.
[2]任丰原,黄海宁,林闯.无线传感器网络.软件学报,.2003,14(7):1282-1291.
[3]孙利民,李建中,陈渝,朱红松.无线传感器网络.清华大学出版社,2005:1-58.
[4]李建中李金宝石胜飞.传感器网络及其数据管理的概念、问题与进展[J].软件学报,2003,14(10):1717-1727.
[5] Y.Zhang, W.Lee. Intrusion detection in wireless ad-hoc networks. Proceedings of Mobi Com 2000. Sixth Annual International Conference on Mobile Computing and Networking,Boston,MA,USA,2000.New York,NY,USA,ACM,2000:275-283.
[6] Dora Cai David , David Clutter , Greg Pape , Jiawei Han. MAIDS: Mining Alarming Incidents from Data Streams. http://www.cs.uiuc.edu/~hanj/pdf/sigmod04_maids.pdf.DBLP.
[7]裵庆祺,沈玉龙,马建峰.无线传感器网络安全技术综述.通信学报.2007,28(8):113-122.
[8]蒋建春,马恒太,任党恩,卿斯汉.网络安全入侵检测:研究综述,软件学报,2000,11(11):1460-1466.
[9]张天成岳德君于戈林树宽谷峪.数据流挖掘研究及其进展[J].小型微型计算机系统,2008,29(12):2241-2246.
[10]孙云李舟军陈火旺.孤立点检测算法及其在数据流挖掘中的可用性[J].计算机科学,2007,34(10):200-203,225.
[11]王超倪志伟朱小虎.基于Squeezer算法的数据流离群数据挖掘算法[J].计算机技术与发展,2008(1):87-89,92.
[12]倪巍伟陆介平陈耿孙志挥.基于k均值分区的数据流离群点检测算法[J].计算机研究与发展,2006,43(9):1639-1643.
[13] Y.Zhuang,L.Chen.In-network Outlier Cleaning for Data Collection in Sensor Networks. The 1st Inernational VLDB Workshop on Clean Databases(CleanDB), Seoul,Korea,2006:97-104.
[14] M.C.Jun,H.Jeong and C.J.Kuo.Distributed Spatio-temporal Outlier Detection in Sensor Networks.Proceedings of SPIE,the International Society for Optical Engineering,2005:273-284.
[15] D.Janakiram,A.M.Reddy and A.P.Kumar.Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks.First International Conference on Communication System Software and Middleware(Comsware),Delhi,India, 2006:1-6.
[16] Sharmila Subramaniam,Themis Palpanas,Dimitris Papadopoulos,Vana Kalogeraki,Dimitrios Gunopulos.Online Outlier Detection in Sensor Data Using Non-Parametric Models. In:Proceedings of the 32nd International Conference on Very Large Data Bases(VLDB), Seoul,Korea.ACM Press,2006,187–198.
[17]杨宜东孙志挥张净.基于核密度估计的分布数据流离群点检测[J].计算机研究与发展,2005,42(9):1498-1504.
[18]王颖李国瑞.基于分组的无线传感器网络入侵检测方案[J].传感技术学报,2009(6):878-882.
[19]周贤伟王培覃伯平申吉红.一种无线传感器网络异常检测技术研究[J].传感技术学报,2007,20(8):1870-1874.
[20]包剑冀常鹏李义杰:.基于移动Agent的分布式入侵检测系统研究[J].长春理工大学学报,2006,29(4):80-83.
[21]张聚伟孙雨耕杨挺.基于D-S证据理论的无线传感器网络MAC层安全研究[J].计算机应用研究,2009,26(1):330-333.
[22]张勇.无线传感器网络中基于博弈论的入侵检测系统[J].微型电脑应用,2009(8):9-11.
[23]薛安荣姚林鞠时光陈伟鹤马汉达.离群点挖掘方法综述[J].计算机科学,2008,35(11):13-18,27.
[24]朱涛江,邹红霞,林琪.Javaservlet JSP经典实例(第1版)[M].北京:中国电力出版社,2005.
[25]王颖李国瑞.基于分组的无线传感器网络入侵检测方案[J].传感技术学报,2009(6):878-882.
[26] Sun B, Osborne L, Xiao Y, Guizani S. Intrusion Detection Techniques in Mobile Ad Hoc and Wireless Sensor Networks [J]. IEEE Wireless Communications, 2007, 10:56-63.
[27]王永利.数据流概要与数据流分析若干关键问题研究[D].东南大学博士论文,2006年.
[28]吴广君云晓春余翔湛王树鹏.网络数据流分段存储模型的研究与实现[J].通信学报,2007,28(12):66-71.
[29]张重庆,李明禄,伍民友.数据收集传感器网络的负载平衡网络构建方法.软件学报,2007,18(5):1110-1121.
[30]周贤伟,覃伯平,徐福华.无线传感器网络与安全.国防工业出版社,2007:179-195.
[31] Inter Berkeley Lab. http://berkeley.intel-research.net/.
[2]任丰原,黄海宁,林闯.无线传感器网络.软件学报,.2003,14(7):1282-1291.
[3]孙利民,李建中,陈渝,朱红松.无线传感器网络.清华大学出版社,2005:1-58.
[4]李建中李金宝石胜飞.传感器网络及其数据管理的概念、问题与进展[J].软件学报,2003,14(10):1717-1727.
[5] Y.Zhang, W.Lee. Intrusion detection in wireless ad-hoc networks. Proceedings of Mobi Com 2000. Sixth Annual International Conference on Mobile Computing and Networking,Boston,MA,USA,2000.New York,NY,USA,ACM,2000:275-283.
[6] Dora Cai David , David Clutter , Greg Pape , Jiawei Han. MAIDS: Mining Alarming Incidents from Data Streams. http://www.cs.uiuc.edu/~hanj/pdf/sigmod04_maids.pdf.DBLP.
[7]裵庆祺,沈玉龙,马建峰.无线传感器网络安全技术综述.通信学报.2007,28(8):113-122.
[8]蒋建春,马恒太,任党恩,卿斯汉.网络安全入侵检测:研究综述,软件学报,2000,11(11):1460-1466.
[9]张天成岳德君于戈林树宽谷峪.数据流挖掘研究及其进展[J].小型微型计算机系统,2008,29(12):2241-2246.
[10]孙云李舟军陈火旺.孤立点检测算法及其在数据流挖掘中的可用性[J].计算机科学,2007,34(10):200-203,225.
[11]王超倪志伟朱小虎.基于Squeezer算法的数据流离群数据挖掘算法[J].计算机技术与发展,2008(1):87-89,92.
[12]倪巍伟陆介平陈耿孙志挥.基于k均值分区的数据流离群点检测算法[J].计算机研究与发展,2006,43(9):1639-1643.
[13] Y.Zhuang,L.Chen.In-network Outlier Cleaning for Data Collection in Sensor Networks. The 1st Inernational VLDB Workshop on Clean Databases(CleanDB), Seoul,Korea,2006:97-104.
[14] M.C.Jun,H.Jeong and C.J.Kuo.Distributed Spatio-temporal Outlier Detection in Sensor Networks.Proceedings of SPIE,the International Society for Optical Engineering,2005:273-284.
[15] D.Janakiram,A.M.Reddy and A.P.Kumar.Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks.First International Conference on Communication System Software and Middleware(Comsware),Delhi,India, 2006:1-6.
[16] Sharmila Subramaniam,Themis Palpanas,Dimitris Papadopoulos,Vana Kalogeraki,Dimitrios Gunopulos.Online Outlier Detection in Sensor Data Using Non-Parametric Models. In:Proceedings of the 32nd International Conference on Very Large Data Bases(VLDB), Seoul,Korea.ACM Press,2006,187–198.
[17]杨宜东孙志挥张净.基于核密度估计的分布数据流离群点检测[J].计算机研究与发展,2005,42(9):1498-1504.
[18]王颖李国瑞.基于分组的无线传感器网络入侵检测方案[J].传感技术学报,2009(6):878-882.
[19]周贤伟王培覃伯平申吉红.一种无线传感器网络异常检测技术研究[J].传感技术学报,2007,20(8):1870-1874.
[20]包剑冀常鹏李义杰:.基于移动Agent的分布式入侵检测系统研究[J].长春理工大学学报,2006,29(4):80-83.
[21]张聚伟孙雨耕杨挺.基于D-S证据理论的无线传感器网络MAC层安全研究[J].计算机应用研究,2009,26(1):330-333.
[22]张勇.无线传感器网络中基于博弈论的入侵检测系统[J].微型电脑应用,2009(8):9-11.
[23]薛安荣姚林鞠时光陈伟鹤马汉达.离群点挖掘方法综述[J].计算机科学,2008,35(11):13-18,27.
[24]朱涛江,邹红霞,林琪.Javaservlet JSP经典实例(第1版)[M].北京:中国电力出版社,2005.
[25]王颖李国瑞.基于分组的无线传感器网络入侵检测方案[J].传感技术学报,2009(6):878-882.
[26] Sun B, Osborne L, Xiao Y, Guizani S. Intrusion Detection Techniques in Mobile Ad Hoc and Wireless Sensor Networks [J]. IEEE Wireless Communications, 2007, 10:56-63.
[27]王永利.数据流概要与数据流分析若干关键问题研究[D].东南大学博士论文,2006年.
[28]吴广君云晓春余翔湛王树鹏.网络数据流分段存储模型的研究与实现[J].通信学报,2007,28(12):66-71.
[29]张重庆,李明禄,伍民友.数据收集传感器网络的负载平衡网络构建方法.软件学报,2007,18(5):1110-1121.
[30]周贤伟,覃伯平,徐福华.无线传感器网络与安全.国防工业出版社,2007:179-195.
[31] Inter Berkeley Lab. http://berkeley.intel-research.net/.