Inductive Conformal Martingales for Change-Point Detection

Volkhonskiy, Denis; Nouretdinov, Ilia; Gammerman, Alexander; Vovk, Vladimir; Burnaev, Evgeny

doi:10.48550/arxiv.1706.03415

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…A probabilistic approach was proposed by Olsson and Holst (2015) that aggregates point outliers into group (i.e., collective) anomalies. Several other methods used martingales to convert nonconformity measures to change-point estimates (Ho, 2005;Ho and Wechsler, 2010;Volkhonskiy et al, 2017).…”

Section: Anomaly Scoringmentioning

confidence: 99%

No free lunch but a cheaper supper: A general framework for streaming anomaly detection

Calikus

Nowaczyk

Sant'Anna

et al. 2020

Expert Systems with Applications

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In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-sizefits-all solution model where a single anomaly detector can detect all anomalies in any domain. To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. On the other hand, ad hoc approaches that are designed for a specific application lack flexibility. Adapting an existing algorithm is not straightforward if the specific constraints or requirements for the existing task change. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI.

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Section: Anomaly Scoringmentioning

confidence: 99%

No free lunch but a cheaper supper: A general framework for streaming anomaly detection

Calikus

Nowaczyk

Sant'Anna

et al. 2020

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…Hence, the time series, data mining and machine learning need to pay special attention to this area as DL MTS regression models are gaining popularity in the safety and cost-critical application domains. A potential idea for the detection of adversarial examples in MTS DL regression models can be the use of inductive conformal anomaly detection method [87], [88]. Another potential idea is to leverage the decades of research into non-probabilistic classifiers, such as the nearest neighbor coupled with DTW [64].…”

Section: E Defense Against Adversarial Attacksmentioning

confidence: 99%

Adversarial Examples in Deep Learning for Multivariate Time Series Regression

Mode¹,

Hoque²

2020

Preprint

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Multivariate time series (MTS) regression tasks are common in many real-world data mining applications including finance, cybersecurity, energy, healthcare, prognostics, and many others. Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications. Unfortunately, DL algorithms are known for their susceptibility to adversarial examples which also makes the DL regression models for MTS forecasting also vulnerable to those attacks. To the best of our knowledge, no previous work has explored the vulnerability of DL MTS regression models to adversarial time series examples, which is an important step, specifically when the forecasting from such models is used in safetycritical and cost-critical applications. In this work, we leverage existing adversarial attack generation techniques from the image classification domain and craft adversarial multivariate time series examples for three state-of-the-art deep learning regression models, specifically Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We evaluate our study using Google stock and household power consumption dataset. The obtained results show that all the evaluated DL regression models are vulnerable to adversarial attacks, transferable, and thus can lead to catastrophic consequences in safety-critical and cost-critical domains, such as energy and finance.

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Inductive Conformal Martingales for Change-Point Detection

Cited by 2 publications

References 16 publications

No free lunch but a cheaper supper: A general framework for streaming anomaly detection

No free lunch but a cheaper supper: A general framework for streaming anomaly detection

Adversarial Examples in Deep Learning for Multivariate Time Series Regression

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