无线传感器网络入侵检测研究
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摘要
无线传感器网络是一种集成了传感器技术、微机微电子技术、无线通信技术和分布式处理技术的下一代新兴网络。其在军事、医疗、环境保护等方面的应用也日趋增多,而随之而来的安全问题也成为困扰无线传感器网络广泛应用的一大障碍。传统的安防措施如防火墙、身份验证及数据加密等虽然都比较成熟,但仍然有很多缺陷。于是入侵检测技术作为可弥补上述传统入侵预防技术的另一有力方式成为了当前无线传感器领域广泛研究的课题之一。
由于无线传感器网络节点受到各种资源的限制,如能耗限制,计算能力不强等,所以在面临真实环境中的各种入侵行为时,容易成为敌方的主要攻击目标。本文分析了无线传感器网络入侵检测技术的研究现状,并对无线传感器网络面临的攻击行为和特征进行了分析。对构建无线传感器网络入侵检测系统所面临的关键性问题进行总结。即无线传感器网络受能量制约以致系统检测的实时性差和效率低下这两大问题。提出了双层分簇无线传感器网络入侵检测系统的架构。在簇内检测阶段使用轻量级的基于马氏距离的异常检测算法,该算法对多个网络属性进行收集处理,考虑了多种攻击手段对网络属性的影响,对簇内恶性程度较高的入侵节点在短时间内进行检测,提高了系统实时性。
由于传感器节点能量有限,簇内检测的强度不宜过高,为了弥补簇内检测的准确率有限的缺陷,本文在Sink节点也部署了已有的成熟入侵检测算法,和簇内检测一同组成双层分簇无线传感器网络入侵检测系统。同时针对检测节点上层应用较多这一实际情况,运用本文提出的基于剩余能量的簇内检测节点选取算法为检测节点的选取提供了很好的能量基础。
本文在系统设计后选用TOSSIM+Graphviz的实验仿真平台,结合专业的KDD CUP99数据集作为模拟入侵的基础数据,对本文提出的系统和内部入侵检测系统进行进行了实验对比。结果证明本文提出的系统能有效提高检测的准确性和降低节点的误警率。
最后,本文总结了研究成果,阐明了未来的研究方向。
Wireless sensor networks(WSN), which intergrate the technologies of sensor, micro-electro-mechanism system, wireless communication,and distributed computing, are the next generation emerging network.The applications of wireless sensor networks in military, medical treatment and environmental protection, etc became more and more, But the following security problem also becomes a big barrier of widespread WSN application. Though a variety of traditonal security mechanisms, such as firewall, authentification,and data encryption, are applied in wireless networks widely, they have many defects. The intrusion detection technology, as a supplement, can make up for deficiency of intrusion prevention approaches,which is one of the hotspot problems in WSN.
Because WSN node constraints by various resource, such as energy consumption restriction, computing capablility, In the case of various intrusion behavior, it is easy to be invading main target. In this paper, current WSN intrusion detection technology analyzed firstly. The status of WSN faces intrusion behavior and characteristics are surveyed in details. Then, paper reviews the two major problem, which is bad Instantaneity and low efficiency caused by energy consumption restriction, in WSN instrusion detection system. According to the content of the above, double clumping wireless sensor network intrusion detection system structure is proposed in this paper. We make use of lightweight Mahalanobis Distance to identify attackers in the cluster. This methods collect multiple network attributes. Multiple attributes of the sensor nodes are taken into consideration to find the pernicious attacker in the cluster.
Due to the limited energy of sensor node, the instrusion detection in the cluster use a lightweight algorithm. In order to improve accuracy of the whole instrusion detection system, this system include the Sink node. A complex intrusion detection algorithm is used in the Sink node since it has enough resources to make up of double clumps wireless sensor network intrusion detection system together with cluster detection. Because of various upper application, this paper proposed the detection node selection algorithm based on the remaining energy to improve energy consumption of detection node.
This paper use TOSSIM + Graphviz simulation platform as experiment entironment after system design and combining professional CUP99 data set which is a base data of simulation aggressive behavior to make a comparison between our algorithm and other intrusion detection algorithm. The experiment shows that our system can achieve highter detection accracy rate and get a lower false alarm rate.
Finally, this paper summarizes the research achievements and illuminates the future research direction.
由于无线传感器网络节点受到各种资源的限制,如能耗限制,计算能力不强等,所以在面临真实环境中的各种入侵行为时,容易成为敌方的主要攻击目标。本文分析了无线传感器网络入侵检测技术的研究现状,并对无线传感器网络面临的攻击行为和特征进行了分析。对构建无线传感器网络入侵检测系统所面临的关键性问题进行总结。即无线传感器网络受能量制约以致系统检测的实时性差和效率低下这两大问题。提出了双层分簇无线传感器网络入侵检测系统的架构。在簇内检测阶段使用轻量级的基于马氏距离的异常检测算法,该算法对多个网络属性进行收集处理,考虑了多种攻击手段对网络属性的影响,对簇内恶性程度较高的入侵节点在短时间内进行检测,提高了系统实时性。
由于传感器节点能量有限,簇内检测的强度不宜过高,为了弥补簇内检测的准确率有限的缺陷,本文在Sink节点也部署了已有的成熟入侵检测算法,和簇内检测一同组成双层分簇无线传感器网络入侵检测系统。同时针对检测节点上层应用较多这一实际情况,运用本文提出的基于剩余能量的簇内检测节点选取算法为检测节点的选取提供了很好的能量基础。
本文在系统设计后选用TOSSIM+Graphviz的实验仿真平台,结合专业的KDD CUP99数据集作为模拟入侵的基础数据,对本文提出的系统和内部入侵检测系统进行进行了实验对比。结果证明本文提出的系统能有效提高检测的准确性和降低节点的误警率。
最后,本文总结了研究成果,阐明了未来的研究方向。
Wireless sensor networks(WSN), which intergrate the technologies of sensor, micro-electro-mechanism system, wireless communication,and distributed computing, are the next generation emerging network.The applications of wireless sensor networks in military, medical treatment and environmental protection, etc became more and more, But the following security problem also becomes a big barrier of widespread WSN application. Though a variety of traditonal security mechanisms, such as firewall, authentification,and data encryption, are applied in wireless networks widely, they have many defects. The intrusion detection technology, as a supplement, can make up for deficiency of intrusion prevention approaches,which is one of the hotspot problems in WSN.
Because WSN node constraints by various resource, such as energy consumption restriction, computing capablility, In the case of various intrusion behavior, it is easy to be invading main target. In this paper, current WSN intrusion detection technology analyzed firstly. The status of WSN faces intrusion behavior and characteristics are surveyed in details. Then, paper reviews the two major problem, which is bad Instantaneity and low efficiency caused by energy consumption restriction, in WSN instrusion detection system. According to the content of the above, double clumping wireless sensor network intrusion detection system structure is proposed in this paper. We make use of lightweight Mahalanobis Distance to identify attackers in the cluster. This methods collect multiple network attributes. Multiple attributes of the sensor nodes are taken into consideration to find the pernicious attacker in the cluster.
Due to the limited energy of sensor node, the instrusion detection in the cluster use a lightweight algorithm. In order to improve accuracy of the whole instrusion detection system, this system include the Sink node. A complex intrusion detection algorithm is used in the Sink node since it has enough resources to make up of double clumps wireless sensor network intrusion detection system together with cluster detection. Because of various upper application, this paper proposed the detection node selection algorithm based on the remaining energy to improve energy consumption of detection node.
This paper use TOSSIM + Graphviz simulation platform as experiment entironment after system design and combining professional CUP99 data set which is a base data of simulation aggressive behavior to make a comparison between our algorithm and other intrusion detection algorithm. The experiment shows that our system can achieve highter detection accracy rate and get a lower false alarm rate.
Finally, this paper summarizes the research achievements and illuminates the future research direction.
引文
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[12] XIAO Renyi, WU Guozheng. A Survey on routing in wireless sensor networks[J]. Progress in Natural Science[J], 2007, 17(3):261-269.
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[21]蹇波,郭永辉,罗长远,李伟.基于ECC的无线传感器网络密钥管理协议[J].计算机工程, 2010, 36(3):142-144.
[22] Vijay Bhuse, Ajay Gupta. Anomaly intrusion detection in wireless sensor networks[J]. Journal of High Speed Networks, 2006, 15(1):33-51.
[23] Ioannis Krontiris, Zinaida Benenson, Thanassis Giannetsos, Felix C. Freiling and Tassos Dimitriou. Cooperative Intrusion Detection in Wireless Sensor Networks[C]. Ireland:Lecture Notes in Computer Science, 2009:263-278.
[24] Soumya Banerjee, Crina Grosan, Ajith Abraham and P.K. Mahanti, Intrusion Detection on Sensor Networks Using Emotional Ants[J], International Journal of Applied Science and Computations, 2005, 12(3):152-173.
[25]王培,周贤伟,覃伯平,赵鹏,郑连存.基于多代理的无线传感器网络入侵检测系统研究[J].传感技术学报, 2007, 20(3):677-681.
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[28] Ioannis Krontiris, Tassos Dimitriou, Thanassis Giannetsos and Marios Mpasoukos. Intrusion Detection of Sinkhole Attacks in Wireless Sensor Networks[C]. Ireland: Lecture Notes in Computer Science, 2008: 150-161.
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