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Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
[article]
2018
arXiv
pre-print
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To overcome this disadvantage and inspired by the recent success of deep learning we propose the application of deep autoencoder neural networks to detect anomalous journal entries. ...
We demonstrate that the trained network's reconstruction error obtainable for a journal entry and regularized by the entry's individual attribute probabilities can be interpreted as a highly adaptive anomaly ...
journal entry x i under optimal model parameters θ * . ...
arXiv:1709.05254v2
fatcat:xsfugembm5au5dxnyz3wcklioy
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
[article]
2019
arXiv
pre-print
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The learned representation provides a holistic view on a given set of journal entries and significantly improves the interpretability of detected accounting anomalies. ...
We demonstrate that such artificial neural networks are capable of learning a semantic meaningful representation of real-world journal entries. ...
We aim to learn a model that detects both classes of anomalous journal entries in an unsupervised manner. ...
arXiv:1908.00734v1
fatcat:iew3huhrprfyzmdfiurkobj5zm
An Attention-Based GRU Network for Anomaly Detection from System Logs
2020
IEICE transactions on information and systems
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Experimental results demonstrate that our proposed approach is effective and achieve better performance than conventional methods. key words: anomaly detection, GRU, attention-based model ...
Concretely, our model adopts a GRU module with attention mechanism to extract the comprehensive and intricate correlations and patterns embedded in a sequence of log entries. ...
Therefore we can model log anomaly detection problem according to the method of language modeling. ...
doi:10.1587/transinf.2020edl8016
fatcat:4mlloda2dncv3owrfubeoaizbq
ANOMALY DETECTION IN THE SERVICES PROVIDED BY MULTI CLOUD ARCHITECTURES: A SURVEY
2014
International Journal of Research in Engineering and Technology
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In the anomaly detection models anomalies are detected by comparing the tracing data with the actual data. ...
Anomaly detection techniques are used to detect and discard anomalies from the data or services. ...
From this entries classification we can identify anomalies i.e. entries available in the failure group. ...
doi:10.15623/ijret.2014.0309030
fatcat:pcty2mkcxbcijdczokrm7b53xu
DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field
2016
Sensors
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This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. ...
However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. ...
., RCNN, the anomaly algorithm should not include feature map entries in the background model that is inside an RCNN bounding box detection. ...
doi:10.3390/s16111904
pmid:27845717
pmcid:PMC5134563
fatcat:iclfqs4rybapdghs7ll4cx7qse
Detecting Anomaly and Its Sources in Activities of Daily Living
2021
SN Computer Science
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Anomalies are detected by modelling the existing activity data representing the usual behavioural routine of an individual to serve as a baseline model. ...
AbstractTo support the independent living and improve the quality of life for the increasing ageing population, system for monitoring their daily routine and detecting anomalies in the routine is required ...
The presented anomaly detection models are applied to ADL datasets for the detection of outliers. ...
doi:10.1007/s42979-020-00418-2
fatcat:ubftvkn3czaatdt6rphx3tzryq
Detection and Implementation of Web-based Attacks using Attribute Length Method
2015
International Journal of Computer Applications
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., learning and detection phase. ...
We have implemented Attribute Length Method proposed by Krugel for the detection of web-based attacks. ...
In that approach authors has used the Markovian model to detect web based attacks. ...
doi:10.5120/21209-3901
fatcat:p2jdpz223jaxnjtwtblzxyyhiu
Evaluating the Content of LMF Standardized Dictionaries
2017
ACM Transactions on Asian and Low-Resource Language Information Processing
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The present paper deals with the detection of anomalies in the content of LMF-standardized dictionaries that covers lexical knowledge at the morphological, syntactic and semantic levels. ...
This approach takes advantage of the LMF fine structure that highlights all kinds of relationships between entries' knowledge and distinguishes the role of each available text such as giving definitions ...
Morphological anomalies In the morphological model, each lexical entry has one lemma, many word forms that represent their inflected forms and morphological features (grammatical number, grammatical gender ...
doi:10.1145/3047406
fatcat:r42f3socgfhlnexkdovops6674
LogLS: Research on System Log Anomaly Detection Method Based on Dual LSTM
2022
Symmetry
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On the other hand, existing automatic log anomaly detection methods are error-prone and often use indices or log templates. ...
and modeled the log according to the preorder relationship and postorder relationship. ...
Figure 2 shows the three main modules of this method: log parsing, log key anomaly detection model, and anomaly detection workflow model. ...
doi:10.3390/sym14030454
fatcat:wugqtv26vnggxevkffeju4bsfm
Log Sequence Anomaly Detection Method Based on Contrastive Adversarial Training and Dual Feature Extraction
2021
Entropy
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detection on the log sequence, with a novel contrastive adversarial training method also used to train the model. ...
First, this paper proposes a log sequence anomaly detection method based on contrastive adversarial training and dual feature extraction. ...
Anomaly detection model.
Figure 5 . 5 Figure 5. Anomaly detection model. ...
doi:10.3390/e24010069
pmid:35052095
pmcid:PMC8774910
fatcat:ji7ofbi4qngo3nf23izfbwrp3i
Comprehensive Review: Intrusion Detection System and Techniques
2016
IOSR Journal of Computer Engineering
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Number of methods is used to secure the system; our focus is on the study of intrusion detection System. ...
Many techniques are used to check the system vulnerabilities and to detect the behavior of the system .i.e. anomalous behavior. Threats on the network become the significant research issue. ...
to model the behavior. detection NIDES It has both misuse or signature based and Network based statistical anomaly anomaly based detection detection Ye et al. ...
doi:10.9790/0661-1804032025
fatcat:rn2eewkctfaqdge5tjbf32bvre
Black Box Anomaly Detection in Multi-Cloud Environment
2016
International Journal of Computer Applications
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detector and secondly anomaly detection is performed. ...
General Terms Anomaly detection in cloud computing. Keywords Black box anomaly detector, cloud service provider, performance diagnosis, cloud systems. ...
We have done survey on anomaly detection in internet as well as cloud computing in which various framework/models are designed. Every framework or model uses an anomaly detection technique. ...
doi:10.5120/ijca2016910125
fatcat:36ub7bhqkjhvdfjzl2lpgq2txi
LLAD: Life-Log Anomaly Detection Based on Recurrent Neural Network LSTM
2021
Journal of Healthcare Engineering
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The results indicate that the proposed model performs more effectively than conventional health data analysis methods, and the proposed approach can yield a satisfactory accuracy in anomaly detection. ...
Therefore, Life-Log analysis methods are important for real-life monitoring and anomaly detection. ...
An RNN-LSTM-based model known as Life-Log Anomaly Detection (LLAD) is proposed herein to effectively detect anomalies in health log data aggregated from several devices. ...
doi:10.1155/2021/8829403
pmid:33708367
pmcid:PMC7932773
fatcat:ar3k5q2z4zglncffcumfm5r7ry
An Unsupervised Anomaly Detection Framework for Detecting Anomalies in Real Time through Network System's Log Files Analysis
2021
High-Confidence Computing
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Research in log-based anomaly detection can be divided into two main categories: batch log-based anomaly detection and streaming log-based anomaly detection. ...
Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies. On the other hand, streaming anomaly detection allows for immediate alert. ...
One of the most prominent online anomaly detection system is DeepLog [17] which trains, offline, a log key anomaly detection model and a parameter value anomaly detection model. ...
doi:10.1016/j.hcc.2021.100030
fatcat:66f55cen7nfyjdw2ehkvmnp66a
ptype: probabilistic type inference
2020
Data mining and knowledge discovery
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In this paper, we propose ptype, a probabilistic robust type inference method that allows us to detect such entries, and infer data types. ...
Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. ...
The proposed model We propose a new probabilistic mixture model with a noisy observation model, allowing us to detect missing and anomalous data entries. ...
doi:10.1007/s10618-020-00680-1
fatcat:kbcuqqrdi5cbjdme2tawvywreu
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