A Novel Anomaly Detection Scheme Based on Principal Component Classifier

By Mei-Ling Shyu, Shu‐Ching Chen, Kanoksri Sarinnapakorn, LiWu Chang. 2003

886 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dcb207ce848b358aeb2e4698c9ea1ad273ce98db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (2022, background): “Clearly, the 30 benchmark datasets would have outliers on the different tails of the distribution, and none of the variants can capture this, although taking the average in ECOD-B alleviates the problem and therefore ranks at the second position.”
Intrusion detection by machine learning: A review (2009, unknown): “…Tian et al. (2004), Wang and Battiti (2006), Zhang and Shen (2005) Schultz et al. (2001), Scott (2004), Wang et al. (2006) Chen et al. (2005), Shyu et al. (2003) Bouzida et al. (2004), Depren et al. (2005), Fan et al. (2004), Peddabachigari et al. (2004) Ramos and Abraham (2005) Balajinath…”
Out-of-distribution Detection with Deep Nearest Neighbors (2022, methodology): “Our comparison covers an extensive collection of outlier detection methods in literature including: IForest (Liu et al., 2008), OCSVM (Sch ¨ olkopf et al., 2001), LODA (Pevn ` y, 2016), PCA (Shyu et al., 2003), and LOF (Breunig et al., 2000).”
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams (2017, methodology): “2001), isolation forest (Liu, Ting, and Zhou 2008) and principle component analysis (PCA) baselines (Shyu et al. 2003).”
Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, methodology): “[91] SubsequenceLOF [22] Data Mining TwoFinger[90] NorM[14] DissimilarityAlgo[6] STOMP [164] Series2Graph [16] HOTSAX [70] STAMP [156] MERLIN[97] DAD[154] BoehmerGraph[13] GrammarViz [120] TARZAN [71] NormA-SJ [15] NormA-smpl[15] KnorrSeq2[102] TSBitmap [144] ILOF[108] VALMOD [82] SurpriseEncoding[2”
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (2021, methodology): “We compare the performance of our proposed method with ﬁve popular anomaly detection methods, including: • PCA: Principal Component Analysis (Shyu et al. 2003) ﬁnds a low-dimensional projection that captures most of the variance in the data.”
Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters (2005, methodology): “An approach for modelling normal trac using self-organising maps is presented in (Gonzalez & Dasgupta 2002), while another one uses principal component classiers to obtain the model ( Shyu, Chen, Sarinnapakorn & Chang 2003 ).”
Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “Machine learning is widely used in anomaly-based NIDS with examples including PHAD (Mahoney and Chan, 2001) and the Principal Component Classiﬁer by Shyu et al. (2003).”
PyOD: A Python Toolbox for Scalable Outlier Detection (2019, background): “, 2003) Proximity Yes No PCA (Shyu et al., 2003) Linear Model No No MCD (Hardin and Rocke, 2004) Linear Model No No Feature Bagging (Lazarevic and Kumar, 2005) Ensembling No Yes ABOD (Kriegel et al.”
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series (2019, methodology): “[23] proposed an approach for anomaly detection based on Principle Component Analysis (PCA), where predictive model is constructed from the major and minor principle components of the normal instances.”