High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

By Sarah Monazam Erfani, Sutharshan Rajasegarar, Shanika A. Karunasekera, C. Leckie. Pattern Recognition, 2016

1,120 citations recorded by the source corpus.

High-dimensional problem domains pose significant challenges for anomaly detection. The presence of irrelevant features can conceal the presence of anomalies. This problem, known as the 'curse of dimensionality', is an obstacle for many anomaly detection techniques. Building a robust anomaly detection model for use in high-dimensional spaces requires the combination of an unsupervised feature extractor and an anomaly detector. While one-class support vector machines are effective at producing decision surfaces from well-behaved feature vectors, they can be inefficient at modelling the variation in large, high-dimensional datasets. Architectures such as deep belief networks (DBNs) are a promising technique for learning robust features. We present a hybrid model where an unsupervised DBN is trained to extract generic underlying features, and a one-class SVM is trained from the features learned by the DBN. Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient. The experimental results show that our proposed model yields comparable anomaly detection performance with a deep autoencoder, while reducing its training and testing time by a factor of 3 and 1000, respectively. HighlightsWe use a combination of a one-class SVM and deep learning.In our model linear kernels can be used rather than nonlinear ones.Our model delivers a comparable accuracy with a deep autoencoder.Our model executes 3times faster in training and 1000 faster than a deep autoencoder.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f076e4355c0facf111716dcab2837803367dd2d8

How researchers see this paper

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

In Artificial Intelligence

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery (2017, positive): “Erfani et al. [4] proposed a hybrid model for unsupervised anomaly detection that uses a one-class support vector machine (SVM).”
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review (2021, methodology): “On network trafﬁc, data clas-siﬁcation algorithms such as Support Vector Machine (SVM) [111], [112], Decision Tree [113], [114], KNN [100] have given important and powerful results.”
Deep Learning for Anomaly Detection: A Survey (2019, methodology): “irregular mixtures of image pixels are detected using a variant of autoencoder model Chalapathy et al. [2018b], Bontemps et al. [2016], Araya et al.”
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications (2018, methodology): “, one-class SVM [1, 7], clustering based methods [9] like K-Means [26] and GMM [21], KDE [27], and VAE [2] and VRNN [33].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “[147] DBN Dimensionality reduction of high dimensional feature set is performed by training a DBN as nonlinear dimensionality reduction tool for human activity recognition using smart phones.”
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, methodology): “Many machine learning techniques, such as matrix factorization [41], [42] and SVM [43], have also been applied to detect graph anomalies.”
Progress in Outlier Detection Techniques: A Survey (2019, background): “They are mostly categorized into the model architecture adopting autoencoders [213] and hybrid models [214].”