基于多粒度级联孤立森林算法的异常检测模型

杨晓晖; 张圣昌

doi:10.11959/j.issn.1000-436x.2019132

您当前的位置：

首页 >

文章列表页 >

基于多粒度级联孤立森林算法的异常检测模型

学术论文 | 更新时间：2024-06-05

- 基于多粒度级联孤立森林算法的异常检测模型
- Anomaly detection model based on multi-grained cascade isolation forest algorithm
- 通信学报 2019年40卷第8期页码：133-142
- 作者机构：
  
  河北大学网络空间安全与计算机学院，河北保定 071002
- 作者简介：
  
  [ "杨晓晖（1975- ），男，河北巨鹿人，博士，河北大学教授、硕士生导师，主要研究方向为分布计算、信息安全与可信计算。" ]
  [ "张圣昌（1993- ），男，河北邯郸人，河北大学硕士生，主要研究方向为分布式计算与信息安全。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFB0802300)
- DOI：10.11959/j.issn.1000-436x.2019132
  中图分类号： TP181
- 网络首发：2019-08，
  
  纸质出版：2019-08-25
- 稿件说明：
移动端阅览
杨晓晖, 张圣昌. 基于多粒度级联孤立森林算法的异常检测模型[J]. 通信学报, 2019,40(8):133-142.

Xiaohui YANG, Shengchang ZHANG. Anomaly detection model based on multi-grained cascade isolation forest algorithm[J]. Journal on Communications, 2019, 40(8): 133-142.
杨晓晖, 张圣昌. 基于多粒度级联孤立森林算法的异常检测模型[J]. 通信学报, 2019,40(8):133-142. DOI： 10.11959/j.issn.1000-436x.2019132.

Xiaohui YANG, Shengchang ZHANG. Anomaly detection model based on multi-grained cascade isolation forest algorithm[J]. Journal on Communications, 2019, 40(8): 133-142. DOI： 10.11959/j.issn.1000-436x.2019132.

摘要

孤立森林算法是基于隔离机制的异常检测算法，存在与轴平行的局部异常点无法检测、对高维数据异常点缺乏敏感性和稳定性等问题。针对这些问题，提出了基于随机超平面的隔离机制和多粒度扫描机制，随机超平面使用多个维度的线性组合简化数据模型的隔离边界，利用随机线性分类器的隔离边界能够检测更复杂的数据模式。同时，多粒度扫描机制利用滑动窗口的方式进行维度子采样，每一个维度子集均训练一个森林，多个森林集成投票决策，构造层次化集成学习异常检测模型。实验表明，改进的孤立森林算法对复杂异常数据模式有更好的稳健性，层次化集成学习模型提高了高维数据中异常检测的准确性和稳定性。

Abstract

The isolation-based anomaly detector

isolation forest has two weaknesses

its inability to detect anomalies that were masked by axis-parallel clusters

and anomalies in high-dimensional data.An isolation mechanism based on random hyperplane and a multi-grained scanning was proposed to overcome these weaknesses.The random hyperplane generated by a linear combination of multiple dimensions was used to simplify the isolation boundary of the data model which was a random linear classifier that can detect more complex data patterns

so that the isolation mechanism was more consistent with data distribution characteristics.The multi-grained scanning was used to perform dimensional sub-sampling which trained multiple forests to generate a hierarchical ensemble anomaly detection model.Experiments show that the improved isolation forest has better robustness to different data patterns and improves the efficiency of anomaly points in high-dimensional data.

关键词

Keywords

references

GRUBBS F . Procedures for detecting outlying observations in samples [J ] . Technometrics , 1969 , 11 ( 1 ): 1 - 21 .

毛嘉莉 , 金澈清 , 章志刚 , 等 . 轨迹大数据异常检测:研究进展及系统框架 [J ] . 软件学报 , 2017 , 28 ( 1 ): 17 - 34 .

MAO J L , JIN C Q , ZHANG Z G , et al . Anomaly detection for trajectory big data:Advancements and framework [J ] . Journal of Software , 2017 , 28 ( 1 ): 17 - 34 .

ZHANG L , LIN J , KARIM R . Adaptive kernel density-based anomaly detection for nonlinear systems [J ] . Knowledge-Based Systems , 2018 , 139 : 50 - 63 .

BREUNIG M M , KRIEGEL H P , NG R T . LOF:identifying density-based local outliers [C ] // ACM SIGMOD International Conference on Management of Data . ACM , 2000 : 93 - 104 .

付培国 , 胡晓惠 . 基于密度偏倚抽样的局部距离异常检测方法 [J ] . 软件学报 , 2017 , 28 ( 10 ): 2625 - 2639 .

FU P G , HU X H . Anomaly detection algorithm based on the local distance of density-based sampling data [J ] . Journal of Software , 2017 , 28 ( 10 ): 2625 - 2639 .

AHMED M , MAHMOOD A N , HU J . A survey of network anomaly detection techniques [J ] . Journal of Network and Computer Applications , 2016 , 60 : 19 - 31 .

李洪成 , 吴晓平 , 严博 . 面向 MANET 异常检测的分布式遗传k-means 研究 [J ] . 通信学报 , 2015 , 36 ( 11 ): 167 - 173 .

LI H C , WU X P , YAN B . Research on distributed genetic k-means for anomaly detection in MANET [J ] . Journal on Communications , 2015 , 36 ( 11 ): 167 - 173 .

唐成华 , 刘鹏程 , 汤申生 , 等 . 基于特征选择的模糊聚类异常入侵行为检测 [J ] . 计算机研究与发展 , 2015 , 52 ( 3 ): 718 - 728 .

TANG C H , LIU P C , TANG S S , et al . Anomaly intrusion behavior detection based on fuzzy clustering and features selection [J ] . Journal of Computer Research and Development , 2015 , 52 ( 3 ): 718 - 728 .

程国振 , 程东年 , 俞定玖 . 基于多尺度低秩模型的网络异常流量检测方法 [J ] . 通信学报 , 2012 , 33 ( 1 ): 182 - 190 .

CHENG G Z , CHENG D N , YU D J . Network traffic detection based on multi-resolution low rank model [J ] . Journal on Communications , 2012 , 33 ( 1 ): 182 - 190 .

张晶 , 冯林 . 针对动态非平衡数据集鲁棒的在线极端学习机 [J ] . 计算机研究与发展 , 2015 , 52 ( 7 ): 1487 - 1498 .

ZHANG J , FENG L . An algorithm of robust online extreme learning machine for dynamic imbalanced datasets [J ] . Journal of Computer Research and Development , 2015 , 52 ( 7 ): 1487 - 1498 .

GOLDSTEIN M , UCHIDA S . A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data [J ] . PloS one , 2016 , 11 ( 4 ):e0152173.

KAI M T , ZHOU G T , LIU F T , et al . Mass estimation and its applications [C ] // ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM , 2010 : 989 - 998 .

TING K M , ZHOU G T , LIU F T , et al . Mass estimation [J ] . Machine Learning , 2013 , 90 ( 1 ): 127 - 160 .

LIU F T , KAI M T , ZHOU Z H . On detecting clustered anomalies using SCiForest [C ] // European Conference on Machine Learning and Knowledge Discovery in Databases . Springer-Verlag , 2010 : 274 - 290 .

BANDARAGODA T R , KAI M T , ALBRECHT D , et al . Isolation‐based anomaly detection using nearest‐neighbor ensembles [J ] . Computational Intelligence , 2018 , 34 ( 4 ): 968 - 998 .

LIU F T , TING K M , ZHOU Z H . Isolation forest [C ] // The IEEE International Conference on Data Mining . IEEE , 2008 : 413 - 422 .

MUJA M , LOWE D G . Scalable nearest neighbor algorithms for high dimensional data [J ] . IEEE Transactions on Pattern Analysis ＆ Machine Intelligence , 2014 ( 11 ): 2227 - 2240 .

DOMINGUES R , FILIPPONE M , MICHIARDI P , et al . A comparative evaluation of outlier detection algorithms:Experiments and analyses [J ] . Pattern Recognition , 2018 ( 74 ): 406 - 421 .

PHAM B T , PRAKASH I , BUI D T . Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classify cation and regression trees [J ] . Geomorphology , 2018 ( 303 ): 256 - 270 .

KRAWCZYK B , MINKU L L , GAMA J , et al . Ensemble learning for data stream analysis:a survey [J ] . Information Fusion , 2017 ( 37 ): 132 - 156 .

ROY G , ROY G , ROY G , et al . Robust random cut forest based anomaly detection on streams [C ] // International Conference on International Conference on Machine Learning.JMLR . org , 2016 : 2712 - 2721 .

浏览量

2612

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于协同对抗增强生成模型的智能无人机网络异常检测方法

自由空间光通信的异常检测、分类与定位深度学习架构

基于图提示微调的WSN节点时空相关性异常检测方法

基于自适应元学习的5G核心网协议字段变异方法

网络异常检测中的流量表示研究