大流量分布式拒绝服务攻击(High-rate Distributed Denial of Service Attack)是指导致网络流量激增,呈明显异常的"淹没受害者"式的DDoS,简称HDDoS。与其相对应的概念是低流量DDoS。通过建立、分析HDDoS的概念模型总结了其特...大流量分布式拒绝服务攻击(High-rate Distributed Denial of Service Attack)是指导致网络流量激增,呈明显异常的"淹没受害者"式的DDoS,简称HDDoS。与其相对应的概念是低流量DDoS。通过建立、分析HDDoS的概念模型总结了其特点、分析了当前HDDoS防御策略的发展趋势。提出了一种基于离群数据挖掘算法的HDDoS防御策略ODM方法。实验证明,ODM方法解决了DDoS过滤中产生的间接伤害无法恢复的问题,是防御HDDoS的一种新思路。展开更多
The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detectio...The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.展开更多
文摘大流量分布式拒绝服务攻击(High-rate Distributed Denial of Service Attack)是指导致网络流量激增,呈明显异常的"淹没受害者"式的DDoS,简称HDDoS。与其相对应的概念是低流量DDoS。通过建立、分析HDDoS的概念模型总结了其特点、分析了当前HDDoS防御策略的发展趋势。提出了一种基于离群数据挖掘算法的HDDoS防御策略ODM方法。实验证明,ODM方法解决了DDoS过滤中产生的间接伤害无法恢复的问题,是防御HDDoS的一种新思路。
基金Project(2011AA040603) supported by the National High Technology Ressarch & Development Program of ChinaProject(201202226) supported by the Natural Science Foundation of Liaoning Province, China
文摘The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.
