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Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks [article]

Marco Schreyer, Timur Sattarov, Damian Borth, Andreas Dengel, Bernd Reimer
2018 arXiv   pre-print
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]

Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer, and Damian Borth
2019 arXiv   pre-print
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

Yixi XIE, Lixin JI, Xiaotao CHENG
2020 IEICE transactions on information and systems  
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


Mahendra Kumar Ahirwar .
2014 International Journal of Research in Engineering and Technology  
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

Peter Christiansen, Lars Nielsen, Kim Steen, Rasmus Jørgensen, Henrik Karstoft
2016 Sensors  
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

Salisu Wada Yahaya, Ahmad Lotfi, Mufti Mahmud
2021 SN Computer Science  
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

Snigdha Agrawal, Priya Gupta, Vanita Jain, Achin Jain
2015 International Journal of Computer Applications  
., 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

Wafa Wali, Bilel Gargouri, Adelmajid Ben Hamadou
2017 ACM Transactions on Asian and Low-Resource Language Information Processing  
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

Yiyong Chen, Nurbol Luktarhan, Dan Lv
2022 Symmetry  
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

Qiaozheng Wang, Xiuguo Zhang, Xuejie Wang, Zhiying Cao
2021 Entropy  
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

Sheenam Sheenam, Sanjeev Dhiman
2016 IOSR Journal of Computer Engineering  
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

Mahendra Kumar, Manish Kumar, Uday Chourasia
2016 International Journal of Computer Applications  
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

Ermal Elbasani, Jeong-Dong Kim, Mihajlo Jakovljevic
2021 Journal of Healthcare Engineering  
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

Vannel Zeufack, Donghyun Kim, Daehee Seo, Ahyoung Lee
2021 High-Confidence Computing  
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

Taha Ceritli, Christopher K. I. Williams, James Geddes
2020 Data mining and knowledge discovery  
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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