[1] |
GRUBBS F . Procedures for detecting outlying observations in samples[J]. Technometrics, 1969,11(1): 1-21.
|
[2] |
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.
|
[3] |
ZHANG L , LIN J , KARIM R . Adaptive kernel density-based anomaly detection for nonlinear systems[J]. Knowledge-Based Systems, 2018,139: 50-63.
|
[4] |
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.
|
[5] |
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.
|
[6] |
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.
|
[7] |
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.
|
[8] |
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.
|
[9] |
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.
|
[10] |
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.
|
[11] |
GOLDSTEIN M , UCHIDA S . A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data[J]. PloS one, 2016,11(4):e0152173.
|
[12] |
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.
|
[13] |
TING K M , ZHOU G T , LIU F T ,et al. Mass estimation[J]. Machine Learning, 2013,90(1): 127-160.
|
[14] |
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.
|
[15] |
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.
|
[16] |
LIU F T , TING K M , ZHOU Z H . Isolation forest[C]// The IEEE International Conference on Data Mining. IEEE, 2008: 413-422.
|
[17] |
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.
|
[18] |
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.
|
[19] |
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.
|
[20] |
KRAWCZYK B , MINKU L L , GAMA J ,et al. Ensemble learning for data stream analysis:a survey[J]. Information Fusion, 2017(37): 132-156.
|
[21] |
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.
|