Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection

Marchi, Erik; Vesperini, Fabio; Weninger, Felix; Eyben, Florian; Squartini, Stefano; Schuller, Björn

doi:10.1109/ijcnn.2015.7280757

Cited by 62 publications

(53 citation statements)

References 25 publications

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“…RNN networks were employed for anomaly detection task in different operational setups [52][53][54][55][56][57]. The authors of [52] proposed an architecture of LSTM-based anomaly detector which incorporates both hierarchical approach and multi-step analysis.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

Wielgosz

Mertik

Skoczeń

et al. 2018

Engineering Applications of Artificial Intelligence

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A B S T R A C TThis paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom Gated Recurrent Unit-based detector and developed a method for the detector parameters selection.Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the dataset intended for real-life experiments and model training was composed of the signals acquired from a new type of magnet, to be used during High-Luminosity Large Hadron Collider project. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art One Class Support Vector Machine (OC-SVM) reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties.It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the setup with the lowest maximum false anomaly length of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

Wielgosz

Mertik

Skoczeń

et al. 2018

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

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“…This means that every time step should include an evaluation of the current value combined with the evolution of precedent information. In this section, we briefly describe work related applying LSTM RNN to time series and collective anomaly detection problems [12,14,15].…”

Section: Related Workmentioning

confidence: 99%

“…Marchi et al [14,13] presented a novel approach by combining non-linear predictive denoising autoencoders (DA) with LSTM for identifying abnormal acoustic signals. Firstly, LSTM Recurrent DA was employed to predict auditory spectral features of the next short-term frame from its previous frames.…”

Section: Related Workmentioning

confidence: 99%

Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks

Bontemps

Cao

McDermott

et al. 2016

Lecture Notes in Computer Science

182

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Intrusion detection for computer network systems becomes one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to its valuable resources on computer networks. Traditional misuse detection strategies are unable to detect new and unknown intrusion. Besides, anomaly detection in network security is aim to distinguish between illegal or malicious events and normal behavior of network systems. Anomaly detection can be considered as a classification problem where it builds models of normal network behavior, which it uses to detect new patterns that significantly deviate from the model. Most of the current research on anomaly detection is based on the learning of normally and anomaly behaviors. They do not take into account the previous, recent events to detect the new incoming one. In this paper, we propose a real time collective anomaly detection model based on neural network learning and feature operating. Normally a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is trained only on normal data and it is capable of predicting several time steps ahead of an input. In our approach, a LSTM RNN is trained with normal time series data before performing a live prediction for each time step. Instead of considering each time step separately, the observation of prediction errors from a certain number of time steps is now proposed as a new idea for detecting collective anomalies. The prediction errors from a number of the latest time steps above a threshold will indicate a collective anomaly. The model is built on a time series version of the KDD 1999 dataset. The experiments demonstrate that it is possible to offer reliable and efficient for collective anomaly detection.

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“…Marchi et al [13,12] presented a novel approach by combining non-linear predictive denoising autoencoders (DA) with LSTM for identifying abnormal acoustic signals. Firstly, LSTM Recurrent DA was employed to predict auditory spectral features of the next short-term frame from its previous frames.…”

Section: Related Workmentioning

confidence: 99%

“…The results demonstrated that their model performed significantly better than existing methods. The idea is also used in a practical acoustic example [13,12], where LSTM RNNs are used to predict shortterm frames.…”

Section: Related Workmentioning

confidence: 99%

One-Class Collective Anomaly Detection Based on LSTM-RNNs

Thi¹,

Cao

Le-Khac

2017

Transactions on Large-Scale Data- And Knowledge-Centered Systems XXXVI

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Intrusion detection for computer network systems has been becoming one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to the valuable resources hosted on computer networks. Traditional misuse detection strategies are unable to detect new and unknown intrusion types. In contrast, anomaly detection in network security aims to distinguish between illegal or malicious events and normal behavior of network systems. Anomaly detection can be considered as a classification problem where it builds models of normal network behavior, of which it uses to detect new patterns that significantly deviate from the model. Most of the current approaches on anomaly detection is based on the learning of normal behavior and anomalous actions. They do not include memory that is they do not take into account previous events classify new ones. In this paper, we propose a one-class collective anomaly detection model based on neural network learning. Normally a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is trained only on normal data, and it is capable of predicting several time-steps ahead of an input. In our approach, a LSTM RNN is trained on normal time series data before performing a prediction for each time-step. Instead of considering each time-step separately, the observation of prediction errors from a certain number of time-steps is now proposed as a new idea for detecting collective anomalies. The prediction errors of a certain number of the latest time-steps above a threshold will indicate a collective anomaly. The model is evaluated on a time series version of the KDD 1999 dataset. The experiments demonstrate that the proposed model is capable to detect collective anomaly efficiently.

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Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection

Cited by 62 publications

References 25 publications

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks

One-Class Collective Anomaly Detection Based on LSTM-RNNs

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