scholarly journals A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hongchao Song ◽  
Zhuqing Jiang ◽  
Aidong Men ◽  
Bo Yang
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Dimensional Space ◽  
High Dimensional Data ◽  
Real Life ◽  
Detection Methods ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Detection Model ◽  
Anomaly Detector

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k-nearest neighbor graphs- (K-NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

scholarly journals NOVEL HYPERSPECTRAL ANOMALY DETECTION METHODS BASED ON UNSUPERVISED NEAREST REGULARIZED SUBSPACE

2018 ◽  
Vol XLII-3 ◽  
pp. 539-546
Author(s):  
Z. Hou ◽  
Y. Chen ◽  
K. Tan ◽  
P. Du
Keyword(s):  
Anomaly Detection ◽  
Spatial Information ◽  
Hyperspectral Imagery ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Outlier Removal ◽  
Residual Image ◽  
Affect Detection ◽  
Anomaly Detector ◽  
Dual Window

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

scholarly journals Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 302
Author(s):  
Chunde Liu ◽  
Xianli Su ◽  
Chuanwen Li
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
Underground Mining ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Clustering Methods ◽  
Safety Warning

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

scholarly journals Multiview deep learning based on tensor decomposition and its application in fault detection of overhead contact systems

2021 ◽  
Author(s):  
Xuewu Zhang ◽  
Yansheng Gong ◽  
Chen Qiao ◽  
Wenfeng Jing
Keyword(s):  
High Speed ◽  
Tensor Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Training Time ◽  
Detection Model ◽  
Railway Line ◽  
Result Show ◽  
Deep Layers

AbstractThis article mainly focuses on the most common types of high-speed railways malfunctions in overhead contact systems, namely, unstressed droppers, foreign-body invasions, and pole number-plate malfunctions, to establish a deep-network detection model. By fusing the feature maps of the shallow and deep layers in the pretraining network, global and local features of the malfunction area are combined to enhance the network's ability of identifying small objects. Further, in order to share the fully connected layers of the pretraining network and reduce the complexity of the model, Tucker tensor decomposition is used to extract features from the fused-feature map. The operation greatly reduces training time. Through the detection of images collected on the Lanxin railway line, experiments result show that the proposed multiview Faster R-CNN based on tensor decomposition had lower miss probability and higher detection accuracy for the three types faults. Compared with object-detection methods YOLOv3, SSD, and the original Faster R-CNN, the average miss probability of the improved Faster R-CNN model in this paper is decreased by 37.83%, 51.27%, and 43.79%, respectively, and average detection accuracy is increased by 3.6%, 9.75%, and 5.9%, respectively.

Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces

2012 ◽  
Vol 8 (2) ◽  
pp. 44-63 ◽  
Author(s):  
Baoxun Xu ◽  
Joshua Zhexue Huang ◽  
Graham Williams ◽  
Qiang Wang ◽  
Yunming Ye
Keyword(s):  
Random Forest ◽  
High Dimensional Data ◽  
Real Life ◽  
Classification Performance ◽  
Feature Weighting ◽  
Random Forest Model ◽  
High Dimensional ◽  
Forest Model ◽  
Forest Models ◽  
Random Forest Models

The selection of feature subspaces for growing decision trees is a key step in building random forest models. However, the common approach using randomly sampling a few features in the subspace is not suitable for high dimensional data consisting of thousands of features, because such data often contains many features which are uninformative to classification, and the random sampling often doesn’t include informative features in the selected subspaces. Consequently, classification performance of the random forest model is significantly affected. In this paper, the authors propose an improved random forest method which uses a novel feature weighting method for subspace selection and therefore enhances classification performance over high-dimensional data. A series of experiments on 9 real life high dimensional datasets demonstrated that using a subspace size of features where M is the total number of features in the dataset, our random forest model significantly outperforms existing random forest models.

scholarly journals Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sai Kiranmayee Samudrala ◽  
Jaroslaw Zola ◽  
Srinivas Aluru ◽  
Baskar Ganapathysubramanian
Keyword(s):  
Dimensionality Reduction ◽  
Organic Solar Cells ◽  
Large Scale ◽  
Parallel Implementation ◽  
High Dimensional Data ◽  
Real Life ◽  
Processing Parameters ◽  
High Dimensional ◽  
Morphology Evolution ◽  
Reduction Techniques

Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

scholarly journals ConAnomaly: Content-Based Anomaly Detection for System Logs

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6125
Author(s):  
Dan Lv ◽  
Nurbol Luktarhan ◽  
Yiyong Chen
Keyword(s):  
Anomaly Detection ◽  
Semantic Information ◽  
Short Term Memory ◽  
Weighted Average ◽  
Detection Methods ◽  
Detection Model ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
System Logs ◽  
System Maintenance

Enterprise systems typically produce a large number of logs to record runtime states and important events. Log anomaly detection is efficient for business management and system maintenance. Most existing log-based anomaly detection methods use log parser to get log event indexes or event templates and then utilize machine learning methods to detect anomalies. However, these methods cannot handle unknown log types and do not take advantage of the log semantic information. In this article, we propose ConAnomaly, a log-based anomaly detection model composed of a log sequence encoder (log2vec) and multi-layer Long Short Term Memory Network (LSTM). We designed log2vec based on the Word2vec model, which first vectorized the words in the log content, then deleted the invalid words through part of speech tagging, and finally obtained the sequence vector by the weighted average method. In this way, ConAnomaly not only captures semantic information in the log but also leverages log sequential relationships. We evaluate our proposed approach on two log datasets. Our experimental results show that ConAnomaly has good stability and can deal with unseen log types to a certain extent, and it provides better performance than most log-based anomaly detection methods.

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