scholarly journals Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3573 ◽  
Author(s):  
YeongHyeon Park ◽  
Il Yun
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Flexible Manufacturing ◽  
Euclidean Distance ◽  
Training Process ◽  
Detection Model ◽  
Manufacturing Line ◽  
Reduced Parameters ◽  
Fast Adaptation ◽  
Manufacturing Environments

Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder–Decoder with operating machine sounds. RNN Encoder–Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder–Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation.

An anomaly detection method based on double encoder–decoder generative adversarial networks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hui Liu ◽  
Tinglong Tang ◽  
Jake Luo ◽  
Meng Zhao ◽  
Baole Zheng ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Latent Variables ◽  
Dimensional Space ◽  
Superior Performance ◽  
Generative Adversarial Networks ◽  
Training Process ◽  
Content Type ◽  
Detection Model ◽  
Adversarial Networks ◽  
Low Dimensional

Purpose This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns are very rare under this condition. Design/methodology/approach The authors propose a new model with double encoder–decoder (DED) generative adversarial networks to detect anomalies when the model is trained without any abnormal patterns. The DED approach is used to map high-dimensional input images to a low-dimensional space, through which the latent variables are obtained. Minimizing the change in the latent variables during the training process helps the model learn the data distribution. Anomaly detection is achieved by calculating the distance between two low-dimensional vectors obtained from two encoders. Findings The proposed method has better accuracy and F1 score when compared with traditional anomaly detection models. Originality/value A new architecture with a DED pipeline is designed to capture the distribution of images in the training process so that anomalous samples are accurately identified. A new weight function is introduced to control the proportion of losses in the encoding reconstruction and adversarial phases to achieve better results. An anomaly detection model is proposed to achieve superior performance against prior state-of-the-art approaches.

scholarly journals Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

2021 ◽  
Vol 21 (3) ◽  
pp. 175-188
Author(s):  
Sumaiya Thaseen Ikram ◽  
Aswani Kumar Cherukuri ◽  
Babu Poorva ◽  
Pamidi Sai Ushasree ◽  
Yishuo Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Network Models ◽  
Neural Network Models ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Techniques

Abstract Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions with maximum accuracy and reduce false alarm rates. The feature extraction is also automated in these techniques. In this paper, an ensemble of different Deep Neural Network (DNN) models like MultiLayer Perceptron (MLP), BackPropagation Network (BPN) and Long Short Term Memory (LSTM) are stacked to build a robust anomaly detection model. The performance of the ensemble model is analysed on different datasets, namely UNSW-NB15 and a campus generated dataset named VIT_SPARC20. Other types of traffic, namely unencrypted normal traffic, normal encrypted traffic, encrypted and unencrypted malicious traffic, are captured in the VIT_SPARC20 dataset. Encrypted normal and malicious traffic of VIT_SPARC20 is categorised by the deep learning models without decrypting its contents, thus preserving the confidentiality and integrity of the data transmitted. XGBoost integrates the results of each deep learning model to achieve higher accuracy. From experimental analysis, it is inferred that UNSW_ NB results in a maximal accuracy of 99.5%. The performance of VIT_SPARC20 in terms of accuracy, precision and recall are 99.4%. 98% and 97%, respectively.

scholarly journals Deep Neural Network with Dropout for Anomaly Detection in Software Defined Networking

2019 ◽  
Vol 8 (11) ◽  
pp. 4084-4090
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Early Stage ◽  
Model Performance ◽  
Software Defined Networking ◽  
Computer Networking ◽  
Detection Model ◽  
Network Intrusion ◽  
Class Labels

A novel anomaly detection-based NIDS is main demand in the computer networking security for discriminating malicious software attack at the early stage. It monitors and analyzes network traffics, checking abnormal behaviors or attack signatures. The detection rate or accuracy is the prerequisite in the network intrusion detection models, also, developing adaptive and flexible model is a critical challenge regarding to unseen attack. This search paper included the deep neural network (DNN) as anomaly detection model can be used within software defined networking (SDN). Dropout technique is used to prevent DNN model from overfitting. Six features have information about the flow were chosen from NSL-KDD dataset to fit and evaluate this model, these data features could be matched to packet-in message header values, also, these features enable the model to be a good generative, and well perform on intrusion recognition issue with a subset of the data. Cross entropy loss function with SoftMax output layer were used for getting the differences between the two different distribution and mapping to multiple class classification covered five class labels, one is normal and the others are attacks (Dos, R2L, U2L and Probe). Accuracy is a comparative metric utilized for assessing the model performance. The results are promising, where accuracy achieved 92.65%.

scholarly journals Anomaly Detection Based on Mining Six Local Data Features and BP Neural Network

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 571 ◽  
Author(s):  
Zhang ◽  
Zhu ◽  
Li ◽  
Wang ◽  
Guo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Anomaly Detection ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Local Data ◽  
Detection Model ◽  
Proposed Model ◽  
Comparison Results ◽  
Local Monotonicity

Key performance indicators (KPIs) are time series with the format of (timestamp, value). The accuracy of KPIs anomaly detection is far beyond our initial expectations sometimes. The reasons include the unbalanced distribution between the normal data and the anomalies as well as the existence of many different types of the KPIs data curves. In this paper, we propose a new anomaly detection model based on mining six local data features as the input of back-propagation (BP) neural network. By means of vectorization description on a normalized dataset innovatively, the local geometric characteristics of one time series curve could be well described in a precise mathematical way. Differing from some traditional statistics data characteristics describing the entire variation situation of one sequence, the six mined local data features give a subtle insight of local dynamics by describing the local monotonicity, the local convexity/concavity, the local inflection property and peaks distribution of one KPI time series. In order to demonstrate the validity of the proposed model, we applied our method on 14 classical KPIs time series datasets. Numerical results show that the new given scheme achieves an average F1-score over 90%. Comparison results show that the proposed model detects the anomaly more precisely.

scholarly journals Video Abnormal Event Detection Based on One-Class Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiangli Xia ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Event Detection ◽  
Data Representation ◽  
Urban Environments ◽  
Layer By Layer ◽  
Abnormal Event Detection ◽  
Detection Model ◽  
Video Feature ◽  
Hidden Layer

Video abnormal event detection is a challenging problem in pattern recognition field. Existing methods usually design the two steps of video feature extraction and anomaly detection model establishment independently, which leads to the failure to achieve the optimal result. As a remedy, a method based on one-class neural network (ONN) is designed for video anomaly detection. The proposed method combines the layer-by-layer data representation capabilities of the autoencoder and good classification capabilities of ONN. The features of the hidden layer are constructed for the specific task of anomaly detection, thereby obtaining a hyperplane to separate all normal samples from abnormal ones. Experimental results show that the proposed method achieves 94.9% frame-level AUC and 94.5% frame-level AUC on the PED1 subset and PED2 subset from the USCD dataset, respectively. In addition, it achieves 80 correct event detections on the Subway dataset. The results confirm the wide applicability and good performance of the proposed method in industrial and urban environments.

scholarly journals Mask Region Based Convolution Neural Network (R-CNN) based Smart System for Anomaly Detection in Pedestrian Walkways

2019 ◽  
Vol 8 (3) ◽  
pp. 2319-2327
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Convolution Neural Network ◽  
Research Application ◽  
Smart System ◽  
Video Understanding ◽  
Detection Process ◽  
Detection Model ◽  
Actual Event ◽  
Coarse Level

Recently, anomaly detection becomes a fascinating research application which usually raises an alarm in scenarios where the event varies from the actual event. Anomaly detection can be treated as a coarse-level video understanding problem that determines the existence of anomalies from habitual events. This paper introduces a new anomaly detection model by the use of Mask region based convolution neural network (R-CNN). The application of mask in the detection process helps to precisely identify the presence of anomalies in the scene. The effectiveness of the Mask R-CNN based anomaly detection model is validated against UCSD anomaly detection dataset. An extensive quantitative and experimental outcome evidently shows the superior nature of the presented model over the compared methods in a significant manner.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Sign in / Sign up

Export Citation Format

Share Document