Convolutional Neural Networks for Unsupervised Anomaly Detection in Text Data

Gorokhov, Oleg; Petrovskiy, Mikhail; Mashechkin, I. V.

doi:10.1007/978-3-319-68935-7_54

Cited by 18 publications

(24 citation statements)

References 8 publications

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“…Convolutional Neural Networks (CNN), are the popular choice of neural networks for analyzing visual imagery (Krizhevsky et al [2012]). CNN's ability to extract complex hidden features from high dimensional data with complex structure has enabled its use as feature extractors in outlier detection for both sequential and image dataset (Gorokhov et al [2017], Kim [2014]). Evaluation of CNN's based frameworks for anomaly detection is currently still an active area of research ).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning for Anomaly Detection: A Survey

Chalapathy,

Chawla

2019

Preprint

382

452

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Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art deep anomaly detection research techniques into different categories based on the underlying assumptions and approach adopted. Within each category, we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. Besides, for each category, we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting deep anomaly detection techniques for real-world problems.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning for Anomaly Detection: A Survey

Chalapathy,

Chawla

2019

Preprint

382

452

View full text Add to dashboard Cite

show abstract

“…CNNs are the most popular choice of neural network for the image processing goals [32]. Extracting complex hidden features from high dimensional data with a complex structure is the main advantage of CNNs, making them suitable feature extractors for sequential and image datasets [33,34]. The extracted deep features were utilized in different applications like image quality assessment [35], skin lesions classification [36], and person re-identification [37].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras

Vosta

Yow

2022

Applied Sciences

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Surveillance cameras have been increasingly used in many public and private spaces in recent years to increase the security of those areas. Although many companies still recruit someone to monitor the cameras, the person recruited is more likely to miss some abnormal events in the camera feeds due to human error. Therefore, monitoring surveillance cameras could be a waste of time and energy. On the other hand, many researchers worked on surveillance data and proposed several methods to detect abnormal events automatically. As a result, if any anomalous happens in front of the surveillance cameras, it can be detected immediately. Therefore, we introduced a model for detecting abnormal events in the surveillance camera feed. In this work, we designed a model by implementing a well-known convolutional neural network (ResNet50) for extracting essential features of each frame of our input stream followed by a particular schema of recurrent neural networks (ConvLSTM) for detecting abnormal events in our time-series dataset. Furthermore, in contrast with previous works, which mainly focused on hand-crafted datasets, our dataset took real-time surveillance camera feeds with different subjects and environments. In addition, we classify normal and abnormal events and show the method’s ability to find the right category for each anomaly. Therefore, we categorized our data into three main and essential categories: the first groups mainly need firefighting service, while the second and third categories are about thefts and violent behaviour. We implemented the proposed method on the UCF-Crime dataset and achieved 81.71% in AUC, higher than other models like C3D on the same dataset. Our future work focuses on adding an attention layer to the existing model to detect more abnormal events.

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“…However, recent studies have shown that CNN-based models perform better than general RNN-based models [43]. The main advantage of CNNs is their ability to extract complicated hidden features from high-dimensional data with complex structures [44], and they can also be adopted to extract features from sequential data.…”

Section: Time-feature Attention-based Convolutional Autoencoder (Tfa-...mentioning

confidence: 99%

Flight Anomaly Detection via a Deep Hybrid Model

Qin

Wang

et al. 2022

Aerospace

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In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.

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Convolutional Neural Networks for Unsupervised Anomaly Detection in Text Data

Cited by 18 publications

References 8 publications

Deep Learning for Anomaly Detection: A Survey

Deep Learning for Anomaly Detection: A Survey

A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras

Flight Anomaly Detection via a Deep Hybrid Model

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