Robust, Deep and Inductive Anomaly Detection (1704.06743v3)

Abstract: PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use as an anomaly detection technique. However, it is limited in this regard by being sensitive to gross perturbations of the input, and by seeking a linear subspace that captures normal behaviour. The first issue has been dealt with by robust PCA, a variant of PCA that explicitly allows for some data points to be arbitrarily corrupted, however, this does not resolve the second issue, and indeed introduces the new issue that one can no longer inductively find anomalies on a test set. This paper addresses both issues in a single model, the robust autoencoder. This method learns a nonlinear subspace that captures the majority of data points, while allowing for some data to have arbitrary corruption. The model is simple to train and leverages recent advances in the optimisation of deep neural networks. Experiments on a range of real-world datasets highlight the model's effectiveness.

• The paper introduces a robust autoencoder model for anomaly detection that learns nonlinear data representations and provides inductive capabilities.
• The model integrates neural networks for nonlinear learning with robust estimation techniques to handle data corruption effectively.
• This method enables practical applications in dynamic real-time systems like finance and cybersecurity due to its inductive nature.

Overview of Robust, Deep and Inductive Anomaly Detection

The paper "Robust, Deep and Inductive Anomaly Detection" introduces a novel approach to anomaly detection, addressing limitations of classical methods like Principal Component Analysis (PCA) and its variant, Robust PCA (RPCA). The proposed method introduces robust autoencoders, which learn a nonlinear subspace that effectively distinguishes between normal and anomalous data in a more adaptable and inductive manner compared to previous methods.

Motivation and Challenges

Anomaly detection is crucial in a variety of fields, requiring robust methods to identify observations that deviate significantly from expected patterns. Traditional techniques such as PCA are limited by their sensitivity to data perturbations and their reliance on linear subspaces, limiting their robustness in the face of gross data corruption. Although RPCA addresses some of these issues, it lacks inductive capabilities, which are necessary for generalizing findings to new data.

Methodology

The paper proposes a robust autoencoder model that combines the power of neural networks with the robustness required for effective anomaly detection. This model features:

• Nonlinear Subspace Learning: Leveraging autoencoders allows learning of complex data representations through nonlinear activation functions, a significant departure from the linear projections of PCA and RPCA.
• Robustness to Gross Anomalies: The model incorporates terms that explicitly capture and mitigate the influence of data corruption, akin to RPCA.
• Inductive Anomaly Detection: Unlike RPCA, which cannot generalize to unseen data, the proposed model can classify new instances, enabling its use in dynamic environments where ongoing learning is necessary.

Empirical Evaluation

The model's efficacy is demonstrated through experiments on datasets such as USPS, CIFAR-10, and a restaurant video background modeling task. The robust autoencoder showed significant improvements over existing methods in identifying anomalies, particularly on complex image datasets where linear methods struggle. Notably, in the CIFAR-10 task, the model excelled at differentiating between nuanced visual distinctions, such as identifying cats among dogs.

Implications and Future Directions

The implications of this research are twofold:

1. Practical Applications: The ability to apply the model inductively in real-time scenarios makes it suitable for industries like finance or cybersecurity, where adaptive anomaly detection is crucial.
2. Theoretical Advancements: By integrating deep learning with robust estimation techniques, this paper extends the theoretical framework for nonlinear matrix decomposition.

Future research directions may include enhancing the interpretability of the model, striving for transparency in the decision-making process of deep networks, and exploring refined optimization strategies to reduce computational complexity.

This paper contributes to the growing body of literature seeking to harmonize robust statistical techniques with modern machine learning capabilities, offering a promising pathway to improved anomaly detection systems.