Deteksi anomali menggunakan teknik deep learning
Mengidentifikasi anomali (atau pencilan) dalam data merupakan tantangan bagi para ilmuwan dan insinyur dari berbagai bidang sains dan teknologi. Meskipun pendeteksian anomali (objek yang mencurigakan tidak mirip dengan kumpulan data utama) telah digunakan sejak lama dan algoritme pertama dikembangkan kembali pada tahun 60-an abad lalu, di bidang ini terdapat banyak masalah yang belum terselesaikan dan masalah yang dihadapi orang-orang di bidang-bidang seperti konsultasi, penilaian bank, keamanan informasi, transaksi keuangan dan perawatan kesehatan. Sehubungan dengan perkembangan pesat algoritma pembelajaran mendalam selama beberapa tahun terakhir, banyak pendekatan modern untuk memecahkan masalah ini telah diusulkan untuk berbagai jenis data yang dipelajari, baik itu gambar, rekaman dari kamera CCTV, data tabular (tentang transaksi keuangan), dll.
- Deep Anomaly Detection (DAD) - :
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precision / ( )
[2] G. Pang .
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Deep learning for feature extraction - , ( ), . DAD.
.2 . φ(·) : X→ Z Z, .
Learning feature representation of normality - φ(·) : X→ Z , , Z .
End-to-end anomaly score learning - end-to-end , anomaly score.
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Deep learning for feature extraction
. , PCA (principal component analysis) [3] random projection [4], , . MLP, , NNs , RNNs ().
, anomaly score .
Learning feature representation of normality
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Generic Normality Feature Learning. . , .
ψ - , l - , ψ, φ ( ), f - .
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DAD , , . [5]
- , , , .
φ_e (.) - , , φ_d (.) - , . s_x (data reconstruction error) .
Generative Adversarial Networks
GANs - , , ( G) , ( D) .
G D - .
DAD , . , , . AnoGAN [6].
Predictability Modeling. .
x̂_(t +1) = ψ (φ (x1 , x2 , · · · , xt ; Θ); W),
l_pred l_adv - , .
, , . . [7]
Self-supervised Classification. , ( - (n - 1) , - , ). . , .
Anomaly Measure-dependent Feature Learning.
φ(·) : X→ Z, .
l - .
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Distance-based Measure. , : DB outliers [8], k-nearest neighbor distance [9] . - , .
One-class Classification-based Measure. , , , . one-class SVM [10], Support Vector Data Description (SVDD) [11].
Clustering-based Measure. , , [12].
End-to-end anomaly score learning
, anomaly score.
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τ (x; Θ) : X→ R , .
Ranking Models. end-to-end . , . Self-trained deep ordinal regression model [13].
Prior-driven Models. - the Bayesian inverse RL-based sequential anomaly detection. - , . [14].
Softmax Models. , . , .
Deviation Networks (end-to-end pipeline) [1]
, G. Pang , . .5 .
function φ - anomaly scoring network, . Reference score generator - , ( ). ( φ(x; Θ) μ_R) deviation loss function L, anomaly scoring network , , .
deviation loss function
y = 1, , y = 0 . , anomaly score , φ(x; Θ), dev(x) , , "a" ( ). .
, , . SOTA-. end-to-end .
[1] Deep Anomaly Detection with Deviation Networks. G. Pang
[2] Deep Learning for Anomaly Detection: A Review. G. Pang
[3] Emmanuel J Candès, Xiaodong Li, Yi Ma, and John Wright. 2011. Robust principal component analysis?
[4] Ping Li, Trevor J Hastie, and Kenneth W Church. 2006. Very sparse random projections.
[5] Alireza Makhzani and Brendan Frey. 2014. K-sparse autoencoders. In ICLR.
[6] Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. 2017. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery.
[7] Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao. 2018. Future frame prediction for anomaly detection–a new baseline.
[8] Edwin M Knorr and Raymond T Ng. 1999. Finding intensional knowledge of distance-based outliers.[9] Fabrizio Angiulli and Clara Pizzuti. 2002. Fast outlier detection in high dimensional spaces.
[10] Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson. 2001. Estimating the support of a high-dimensional distribution.
[11] David MJ Tax and Robert PW Duin. 2004. Support vector data description.
[12] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. 2018. Deep clustering for unsupervised learning of visual features.
[13] Guansong Pang, Cheng Yan, Chunhua Shen, Anton van den Hengel, and Xiao Bai. 2020. Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection.
[14] Andrew Y Ng dan Stuart J Russell. 2000. Algoritma untuk Inverse Reinforcement Learning.