方法
论文
**HBOS
**M. Goldstein, A. Dengel. Histogram-based Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm[C]. In: Wölfl S, editor. KI-2012: Poster and Demo Track. Online;2012. p. 59–63
GMM
X. Yang, L. J. Latecki, D. Pokrajac. Outlier Detection with Globally Optimal Exemplar-Based GMM[C]. Siam International Conference on Data Mining, SDM 2009, April 官方最新中文版的telegram的下载入口在哪里 30 – May 2, 2009, Sparks, Nevada, Usa. DBLP, 2009:145-154
KNN
S. Ramaswamy, R. Rastogi, K. Shim. Efficient Algorithms for Mining Outliers from Large Data Sets [C]. ACM SIGMOD International Conference on Management of Data. ACM, 2000:427-438、F. Angiulli, C. Pizzuti. Fast Outlier Detection in High Dimensional Spaces[C]. European Conference on Principles of Data Mining and Knowledge Discovery. Springer-Verlag, 2002:15-26
最新的中文版telegram的下载的网址是多少
KNN-weight
F. Angiulli, C. Pizzuti. Fast Outlier Detection in High Dimensional Spaces[C]. European Conference on Principles of Data Mining and Knowledge Discovery. Springer-Verlag, 2002:15-26
**LOF
M. M. Breunig. LOF: identifying density-based local outliers[J]. 2000, 29(2):93-104
COF
J. Tang, Z. Chen, A. Fu, D. Cheung. Enhancing Effectiveness of Outlier Detections for Low Density Patterns[C]. Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2002:535-548
INFLO
W. Jin, A. K. H. Tung, J. Han, et al. Ranking Outliers Using Symmetric Neighborhood Relationship[J]. Lecture Notes in Computer Science, 2006, 3918:577-593.
**LoOP
H. P. Kriegel, E. Schubert, A. Zimek. LoOP:local outlier probabilities[C]. ACM, 2009:1649-1652
CBLOF
Z. He, X. Xu, S. Deng. Discovering cluster-based local outliers[J]. Pattern Recognition Letters, 2003, 24(9–10):1641-1650
LFCOF
M Amer, M. Goldstein. Nearest-Neighbor and Clustering based Anomaly Detection Algorithms for RapidMiner[C]. Rapidminer Community Meeting and Conferernce. 2012
CMGOS
M. Goldstein. Anomaly Detection in Large Datasets[M]. 2014
**DBSCAN+GMM
Bigdeli E, Mohammadi M, Raahemi B, et al. A fast and noise resilient cluster-based anomaly detection[J]. Pattern Analysis & Applications, 2017, 20(1):1-17.
**iForest
F. T. Liu, M. T. Kai, Z. H. Zhou. Isolation-Based Anomaly Detection[M]. ACM, 2012, 6 (1) :1-39
**iForest局部异常
官方最新版telegram的下载的入口在哪里
Aryal S, Kai M T, Wells J R, et al. Improving iForest with Relative Mass[C]// Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, 2014:510-521.
**SCiForest
F. T. Liu, M. T. Kai, Z. H. Zhou. Isolation-Based Anomaly Detection[M]. ACM, 2012, 6 (1) :1-39
RRCF
G. Roy, G. Roy, G. Roy, 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
AutoEncoder
S. S. Khan, B. Taati. Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders[J]. Expert Systems with Applications. 2017, 87:280-290
One-class SVM
W. Khreich, B. Khosravifar, A. Hamou-Lhadj, et al. An anomaly detection system based on variable N-gram features and one-class SVM[J]. Information and Software Technology, 2017, 91:186-197
SVDD
D.M.J Tax, R.P.W. Duin. Support vector domain description. Pattern Recognition Letters[J]. 1999, vol.20:1191-1199
SVM
Wang G P, Yang J X, Li R. Imbalanced SVM‐Based Anomaly Detection Algorithm forImbalanced Training Datasets[J]. Etri Journal, 2017, 39(5):621-631.
**随机森林
Liu D, Zhao Y, Xu H, et al. Opprentice:Towards Practical and Automatic Anomaly Detection Through Machine 官网最新版telegram下载的网站 Learning[C]// Internet Measurement Conference. ACM, 2015:211-224.
PCA
Xu W, Huang L, Fox A, et al. Detecting large-scale system problems by mining console logs[C]// ACM Sigops, Symposium on Operating Systems Principles. ACM, 2009:117-132.
**系统日志
He S, Zhu J, He P, et al. Experience Report: System Log Analysis for Anomaly Detection[C]// IEEE, International Symposium on Software Reliability Engineering. IEEE, 2016:207-218.**
**LSTM无监督
Du M, Li F, Zheng G, et al. DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning[C]// ACM Sigsac Conference on Computer and Communications Security. ACM, 2017:1285-1298.
**深度信念网络+one-class SVM
Erfani S M, Rajasegarar S, Karunasekera S, et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J]. Pattern Recognition, 2016, 58(C):121-134.
**无监督异常检测方法测评
Goldstein M, Uchida S. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data[J]. Plos One, 2016, 11(4):e0152173.