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基于蚂蚁聚类的自适应拒绝服务攻击检测技术 被引量:1

Adaptive DoS detection technique based on ant clustering
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摘要 提出了基于蚂蚁聚类的自适应拒绝服务攻击检测技术,根据DoS/DdoS洪泛攻击往往打破正常网络流量分布平衡的原理,运用自适应蚂蚁聚类算法(AAC)在正常网络情况下生成检测模型,利用已知模型进行自动监控检测,具有良好的适应性和有效性.
作者 杨一 郑建德
机构地区 厦门大学
出处 《通信学报》 EI CSCD 北大核心 2006年第z1期88-91,共4页 Journal on Communications
基金 厦门大学985二期科技创新平台基金资助项目
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参考文献2

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  • 2[4]ERTOZ L,EILERTSON E,LAZAREVIC A,et al.Detection and summarization of novel network attacks using data mining[A].Technical Report[S].2003

同被引文献14

  • 1孙知信,唐益慰,张伟,宫婧,王汝传.基于特征聚类的路由器异常流量过滤算法[J].软件学报,2006,17(2):295-304. 被引量:15
  • 2XU K, ZHANG Z L, BHATTACHARYYA S. Intemet traffic behavior profiling for network security monitoring[J]. IEEE/ACM Transactions on Networking, 2008, 16(6):1241-1252.
  • 3LAKHINA A, CROVELLA M, DIOT C. Mining anomalies using traffic feature distrlbutlons[A]. SIGCOMM[C]. Philadelphia, Pennsylvania, USA, 2005.134-145.
  • 4TORRES R, HAJJAT M, RAO SG, et al. Inferring undesirable behavior fiom P2P traffic analysis[A]. SIGMETRICS[C]. USA, 2009. 231-242.
  • 5GU G, PERDISCI R, ZHANG J, et al. BotMiner: clustering analysis of network traffic for protocol and structure-independent botnet detection[A]. USENIX Security[C]. USA., 2008.67-76.
  • 6DENNING D. An intrusion-detection model[J]. IEEE Transactions on Software Engineering, 1987, 13(2):222-232.
  • 7SOULE A, SALAMATIAN K E, TAFT N. Combining filtering and statistical methods for anomaly detection[A]. IMC[C]. Boston, USA, 2005. 311-322.
  • 8LI M, LI J, ZHAO W. Experimental study of DDOS attacking of flood Type based on NS2[J]. Int J Electronics and Computers, 2009, 1(2): 143-152.
  • 9LI M. Change trend of averaged hurst parameter of traffic under DDOS flood attacks[J). Computers & Security,2006, 25(3): 213-220.
  • 10VARDI. Network Tomography: Estimating source-destination traffic intensities from link data[J]. Journal of the American Statistical Association, 1996, 91(2): 365-377.

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