An attack pattern mining algorithm based on fuzzy logic and sequence pattern

机译：一种基于模糊逻辑和序列模式的攻击模式挖掘算法

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How to get the correlation rules is one of the main challenges in alert correlation research fields. In this paper, we propose an attack pattern mining algorithm to solve this problem. Our method can be divided into two steps: Fast Fuzzy Cluster Analysis (FFCA) and Frequent Sequence Mining (FSM). FFCA can accurately describe the similarity among the alerts attributes accurately, while FSM can dig the correlation between alerts. In order to find the hidden attack patterns behind massive data efficiently and accurately, we combines the characteristics and advantages of them in our method. At first we design the similarity function for each attribute and separate the raw sequence into alert cluster sets through fuzzy cluster based on the similarity function. Then we dig the Frequent Sequences from these cluster sets. Finally we use experiment results to show the feasibility of our method.

机译：如何获得相关规则是警报相关研究领域的主要挑战之一。在本文中，我们提出了一种攻击模式挖掘算法来解决这个问题。我们的方法可分为两个步骤：快速模糊聚类分析（FFCA）和频繁序列挖掘（FSM）。 FFCA可以准确地描述警报属性之间的相似性，而FSM可以在警报之间挖掘相关性。为了有效准确地找到大规模数据后面的隐藏攻击模式，我们将它们的特点和优点与我们的方法相结合。首先，我们设计每个属性的相似性函数，并通过基于相似性函数通过模糊群集将原始序列分成警报群集。然后我们挖掘这些簇集中的频繁序列。最后，我们使用实验结果来显示我们方法的可行性。

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《IEEE International Conference on Cloud Computing and Intelligence Systems》|2016年|500p|共5页
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Yang Li; Ying Xue; Yuangang Yao; Xianghui Zhao; Jianyi Liu; Ru Zhang;
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中图分类 TP393-53;
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Decision support systems;

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专利

1. Algorithm for Mining Web Access Patterns Based on User Access Sequences [J] . Sun Jinhua, Meng Zhaorui, Xie Yanqi The Open Cybernetics & Systemics Journal . 2017,第1期

机译：基于用户访问序列的Web访问模式挖掘算法
2. Mining Fuzzy Sequential Patterns with Fuzzy Time-Intervals in Quantitative Sequence Databases [J] . Truong Duc Phuong, Do Van Thanh, Nguyen Duc Dung Cybernetics and information technologies: CIT . 2017,第2期

机译：定量序列数据库中具有模糊时间间隔的模糊序列模式的挖掘
3. A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS [J] . R. B. V. SUBRAMANYAM, A. GOSWAMI International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems . 2005,第6期

机译：定量时序模式增量挖掘的模糊数据挖掘算法
4. An attack pattern mining algorithm based on fuzzy logic and sequence pattern [C] . Yang Li, Ying Xue, Yuangang Yao, IEEE International Conference on Cloud Computing and Intelligent Systems . 2016

机译：基于模糊逻辑和序列模式的攻击模式挖掘算法
5. Mining frequent patterns from sequences: Theory, algorithm, implementation, and performance. [D] . Turkia, Markus Petteri. 2007

机译：从序列中挖掘频繁的模式：理论，算法，实现和性能。
6. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic [O] . Shi-wang Hou, Shunxiao Feng, Hui Wang 2016

机译：基于模糊逻辑的智能过程异常模式识别与诊断
7. Figure 4: (A) One conserved sequence, which occurs 79 times in 46,264 binding site peaks from the ChIP-seq data-set. The mutation profile of this conserved sequence is illustrated, where ’_ ’ indicates this base is unchanged; DEL indicates this base is lost; INS X indicates a new base X is inserted in front of this base. (B) Several repeated elements patterns are listed. (C) In the first column, the top five DNA motifs, mined by meme-chip tools (Machanick Bailey, 2011) are illustrated. The resemblant conserved sequences, found by the CFSP algorithm are listed in the second column. In the third column, the position-specific scoring matrices, which are transformed from mutational information are listed. The similarity between meme motif and resemblant conserved sequence with PSSM format was calculated via a stamp motif comparison tool (Mahony Benos, 2007). The E-values for the similarity of those pairs is displayed in the fourth column. (D) One motif is selected in each group clustered by gkmsvm descriptors, and the corresponding motif found by the CFSP algorithm is listed below. (E) There are additional datasets (File No: ENCFF100GRL, ENCFF616IRT, ENCFF870CER, Target: SREBF1) collected from https://www.encodeproject.org. The top two motifs are selected in each file using meme tools, and the corresponding motifs found by our algorithm are listed below. [O] . -1

机译：图4：（a）一种保守序列，其发生在芯片-SEQ数据集中的46,264个结合位点峰值中的79倍。说明了这种保守序列的突变分布，其中'_'表示该碱度不变; del表示此基础丢失; INS X表示新的基础X插入此基础前面。（b）列出了几种重复的元素模式。（c）在第一栏中，示出了由MEME芯片工具（Machanick＆Bailey，2011）开采的前五个DNA主题。由CFSP算法发现的相应保守序列列于第二列中。在第三列中，列出了从突变信息转换的特定位置的评分矩阵。 MEME主题与PSSM格式的相似性与PSSM格式之间的相似性通过邮票图章比较工具（Mahony＆Benos，2007）计算。这些对相似性的电子值显示在第四列中。（d）在由GKMSVM描述符聚集的每个组中选择了一个图案，下面列出了CFSP算法的相应主题。（e）从https://www.encodeproject.org收集的，有附加数据集（文件no：cernff100grl，cenf616irl，conf8.20cer，target：srebf1）。使用MEME工具在每个文件中选择前两个图案，并且我们的算法发现的相应主题如下所示。
8. Detecting and Mining Similarities, Differences and Target Patterns in Sequences of Images Using the PFF, LGG and SPNG Approaches [R] . Bourbakis, D. 2004

机译：使用pFF，LGG和spNG方法检测和挖掘图像序列中的相似性，差异和目标模式

An attack pattern mining algorithm based on fuzzy logic and sequence pattern

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