序列模式

• With massive complex computations , dp algorithm can obtain optimized alignment result , but it may neglect the biological significance in sequences . safss mainly processes frequent subsequence in the sequence , which is easy to discovery the biology significance in the redundant fragments
  Safss尝试从序列的生物学意义入手,比对中主要考察序列的高频子序列而不是分离的单个字符,易于发现隐藏于序列之中的富含生物学意义的序列模式。
• Further more , in order to satisfy the needs of association rule mining algorithm in e - business field , this paper studied the support - ordered tree based association rule mining algorithm foldarm and proposed the concept of sequential support - ordered tree
  为了满足电子商务网站对关联规则挖掘算法的要求，本文研究了基于支持度排序树的foldarm算法，对之进行了改进，提出了序列支持度排序树的概念，并给出了建树算法，使得可以挖掘序列模式。
• In this algorithm , firstly the states relationship between in time series is represented to allen temporal logic , then use a sliding windows to examine the order or occur relationship of states and obtain a particularly sequence . on the basis of the sequence , we developed a called girst model to achieve finding the frequent relationship patterns in multiple time series
  该方法利用allen区间逻辑关系来描述时间序列模式的关联关系，避免了传统方法在关联关系描述的上非同步性；然后通过时间观测窗口，来构造出一种包含并行模式和串行模式的特殊形式模式序列；最后，在此基础上构造一种广义的互关联后继树模型，然后用前面挖掘思路实现关联模式的挖掘。
• In this paper , we study focus on building intrusion detection model based the technique of data mining ( dm ) . firstly , the paper designed a scheme to modeling intrusion detection based dm and bright forward the idea of descriptive model and classified model to intrusion detection . secondly , we designed and implemented a net data collection system with high performance and a scheme to pretreat net data . thirdly , after studying the algorithms to mine association rule and sequence rule in net data , we extended and improved the algorithms according to the characteristic of net data and the field knowledge of intrusion detection
  首先设计了基于数据挖掘技术的入侵检测建模方案，提出使用该技术建立入侵检测描述性模型和分类模型的思想，并用分类判决树建立了入侵检测分类模型；其次，设计和实现了一个高性能的网络数据采集系统和网络数据预处理的方案；然后，在对关联规则挖掘和序列规则挖掘算法进行研究的基础上，结合网络数据的特性和入侵检测领域的知识对算法进行了扩展和改进，挖掘出了网络数据的关联模式和序列模式；最后，研究了描述性模式的应用，并设计出基于模式匹配的入侵检测引擎，该引擎具有误用检测和异常检测功能。
• In this algorithm , firstly the states relationship between in time series is represented to allen temporal logic , then use a sliding windows to examine the order or occur relationship of states and obtain a particularly sequence . on the basis of the sequence , we developed a called girst model to achieve finding the frequent relationship patterns in multiple time series . experiments shows , compared with the previous methods , the method is more simple , efficient and more applied value
  该方法利用allen区间逻辑关系来描述时间序列模式的关联关系，避免了传统方法在关联关系描述上的非同步性；然后通过时间观测窗口，构造出?种包含并行模式和串行模式特殊形式的模式序列；最后，在此基础上构造一种广义的互关联后继树模型，然后用前面挖掘思路实现关联模式的挖掘。
• We introduce data mining technology in data analyzing , and extend algorithms of this system based on exploiting algorithms in data mining , such as conjunction analyzing algorithm and serial mode analyzing algorithm , which can extract security related attributes of system characteristic efficiently , promote the scalability of the system greatly and provide data support for insight research toward the system
  在数据分析中引入了数据挖掘技术，在利用数据挖掘中的关联分析、序列模式分析等算法的基础上，针对本系统对算法进行了扩展，能够有效的提取与安全相关的系统特征属性，大大提高了系统的可扩展性，并且对系统的深入调查提供数据的支持。
• In order to unify stored data - mining results in pattern warehouse , the author probes intensively into the storage methods of data - mining results ( association rules , classification rules , sequential patterns ) . it proposes the storage methods to use the relational algebra to describe . this realizes consistent storage of data - mining results in relational database
  为使数据挖掘结果能统一存储在模式库中，作者对关联规则、分类规则和序列模式挖掘结果的存储方法进行了深入的研究，提出了数据挖掘结果模式的存储方法，并用关系代数进行了描述，在关系数据库中实现了多种数据挖掘结果模式的统一存储。
• Based on the study of prevalent dm technology in the detection of network intrusions and the characters of dos attacks , this paper presents a new idea to detect and defend dos attacks by integrating with packet analysis and flow analysis . that is in addition to traditionally producing association rules and frequent episodes rules from packets and connections , trend analysis algorithm is used to forecast and analyze the network flow , the compared results between forecast values and real values become one of the key attributes of rules to detect dos attacks
  在对网络攻击检测中数据挖掘的应用和dos攻击流特征深入研究的基础上，创新地提出一种将数据包分析和流量分析共同用于dos攻击特征挖掘的思路，即除了运用传统对数据包和通信连接的关联规则挖掘和序列模式挖掘外，额外增加利用趋势分析算法对网络的流量预测和分析，并将流量预测值与实际值比较结果作为dos特征检测规则的关键属性之一。
• To solve these problems , this thesis proposed a new model for the intrusion detection system that based on the data mining . we have discussed some key technical problems and related solutions . we apply some existing algorithms of association analysis , sequence pattern analysis , and data classification to the intrusion detection system
  针对这些问题，本文采用了一种基于数据挖掘技术建立入侵检测系统的方法，讨论了该系统实现中的关键技术及解决方法，将现有的数据挖掘算法中的关联分析、序列模式分析、分类等算法应用于入侵检测系统，对入侵行为提取特征、建立规则，通过对审计数据的处理与这些特征进行匹配，检测入侵，以形成智能化的入侵检测系统。