scholarly journals A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4526
Author(s):  
Hao Sun ◽  
Xuyun Fu ◽  
Shisheng Zhong
Keyword(s):  
Anomaly Detection ◽  
Loss Function ◽  
Detection Method ◽  
Detection Methods ◽  
Density Peak ◽  
Parameter Adjustment ◽  
Weakly Supervised ◽  
Predicted Values ◽  
Density Peak Clustering ◽  
Aero Engines

Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder, composed of an attention mechanism and a long short-term memory neural network, is used to construct the mapping relationship mining model among gas-path parameters. The predicted values of gas-path parameters under the restriction of mapping relationships are obtained. The deviation degree from the original values to the predicted values is regarded as the feature. To force the extracted features to better reflect the anomalies and make full use of weakly supervised labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed. This loss function can be combined with a simple classifier to significantly improve the feature extraction results, in which anomaly samples are more different from normal samples and do not reduce the mining precision. Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the improved density peak clustering method, the local density is enhanced by K-nearest neighbors, and the clustering effect is improved by a new outlier threshold determination method and a new outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering can be improved, and the influence of outliers on the clustering process reduced. By introducing weakly supervised label information and automatically iterating according to clustering and anomaly detection results to update the hyperparameter settings, a weakly supervised anomaly detection method without complex parameter adjustment processes can be implemented. The experimental results demonstrate the superiority of the proposed method.

scholarly journals OUTLIER DETECTION METHOD USE FOR THE NETWORK FLOW ANOMALY DETECTION / IŠSKIRČIŲ RADIMO METODŲ TAIKYMAS ANOMALIJOMS KOMPIUTERIŲ TINKLO PAKETŲ SRAUTUOSE APTIKTI

2016 ◽  
Vol 8 (3) ◽  
pp. 327-333 ◽  
Author(s):  
Rimas Ciplinskas ◽  
Nerijus Paulauskas
Keyword(s):  
Anomaly Detection ◽  
Network Flow ◽  
Detection Method ◽  
Attack Detection ◽  
Detection Methods ◽  
Cyber Attack ◽  
Normal Network ◽  
New Type ◽  
Flow Detection ◽  
F Measure

New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure. Kibernetinių atakų gausa ir įvairovė bei siekis nuo jų apsisaugoti verčia nuolat kurti naujus ir tobulinti jau esamus atakų aptikimo metodus. Kaip rodo praktika, dabartiniai atakų atpažinimo metodai iš esmės veikia pagal antivirusinių programų principą, t.y. sudaromi žinomų atakų šablonai, kuriais remiantis yra aptinkamos atakos, tačiau pagrindinis tokių metodų trūkumas – negalėjimas aptikti naujų, dar nežinomų atakų. Šiai problemai spręsti yra pasitelkiami anomalijų aptikimo metodai, kurie leidžia aptikti nukrypimus nuo normalios tinklo būsenos. Straipsnyje yra pateiktas naujas metodas, leidžiantis aptikti kompiuterių tinklo paketų srauto anomalijas taikant lokalių išskirčių faktorių algoritmą. Atliktas tyrimas leido surasti požymių grupes, kurias taikant anomalūs tinklo srautai yra atpažįstami geriausiai, t. y. pasiekiamos didžiausios tikslumo, atkuriamumo ir F-mato reikšmės.

scholarly journals egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5895
Author(s):  
Jiansu Pu ◽  
Jingwen Zhang ◽  
Hui Shao ◽  
Tingting Zhang ◽  
Yunbo Rao
Keyword(s):  
Social Networks ◽  
Anomaly Detection ◽  
Communication Networks ◽  
Social Communication ◽  
Detection Method ◽  
Detection Methods ◽  
Visualization System ◽  
Egocentric Network ◽  
Unsupervised Anomaly Detection ◽  
The Relationship

The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks.

scholarly journals A Fast Orientation Invariant Detector Based on the One-stage Method

2018 ◽  
Vol 232 ◽  
pp. 04036
Author(s):  
Jun Yin ◽  
Huadong Pan ◽  
Hui Su ◽  
Zhonggeng Liu ◽  
Zhirong Peng
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
High Efficiency ◽  
Detection Method ◽  
State Of The Art ◽  
Orientation Angle ◽  
Detection Methods ◽  
Detection Algorithms ◽  
Bounding Boxes ◽  
The One

We propose an object detection method that predicts the orientation bounding boxes (OBB) to estimate objects locations, scales and orientations based on YOLO (You Only Look Once), which is one of the top detection algorithms performing well both in accuracy and speed. Horizontal bounding boxes(HBB), which are not robust to orientation variances, are used in the existing object detection methods to detect targets. The proposed orientation invariant YOLO (OIYOLO) detector can effectively deal with the bird’s eye viewpoint images where the orientation angles of the objects are arbitrary. In order to estimate the rotated angle of objects, we design a new angle loss function. Therefore, the training of OIYOLO forces the network to learn the annotated orientation angle of objects, making OIYOLO orientation invariances. The proposed approach that predicts OBB can be applied in other detection frameworks. In additional, to evaluate the proposed OIYOLO detector, we create an UAV-DAHUA datasets that annotated with objects locations, scales and orientation angles accurately. Extensive experiments conducted on UAV-DAHUA and DOTA datasets demonstrate that OIYOLO achieves state-of-the-art detection performance with high efficiency comparing with the baseline YOLO algorithms.

scholarly journals Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Li ◽  
Ranran Deng ◽  
Yingjie Zhang ◽  
Zhaoyun Sun ◽  
Xueli Hao ◽  
...  
Keyword(s):  
Crack Detection ◽  
Clustering Algorithm ◽  
Asphalt Pavement ◽  
Fruit Fly ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Density Peak ◽  
3D Data ◽  
Density Peak Clustering ◽  
Pavement Crack Detection

Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.

scholarly journals Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1007
Author(s):  
Yupeng Cao ◽  
Jiangwei Cao ◽  
Zhiguo Zhou ◽  
Zhiwen Liu
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Classification Problem ◽  
Recall Rate ◽  
Detection Methods ◽  
Flight Safety ◽  
Prediction Problem ◽  
Memory Network ◽  
Multidimensional Information ◽  
Short Time

In order to ensure flight safety and eliminate hidden dangers, it is very important to detect aircraft track anomalies, which include track deviations and track outliers. Many existing track anomaly detection methods cannot make full use of multidimensional information of the relevant track. Based on this problem, an aircraft track anomaly detection method based on the combination of the Multidimensional Outlier Descriptor (MOD) and the Bi-directional Long-Short Time Memory network (Bi-LSTM) is proposed in this paper. Firstly, track deviation detection is transformed into the track density classification problem, and then a multidimensional outlier descriptor is designed to detect track deviation. Secondly, track outliers detection is transformed into a prediction problem, and then a Bi-LSTM model is designed to detect track outliers. Experimental results based on real aircraft track data indicate that the accuracy of the proposed method is 96% and the recall rate is 97.36%. It can detect both track deviation and track outliers effectively.

scholarly journals Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms

2021 ◽  
Vol 72 ◽  
pp. 849-899
Author(s):  
Cynthia Freeman ◽  
Jonathan Merriman ◽  
Ian Beaver ◽  
Abdullah Mueen
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Detection Method ◽  
Concept Drift ◽  
Area Under The Curve ◽  
Detection Methods ◽  
Experimental Comparison ◽  
Detection Algorithms ◽  
Extensive Evaluation ◽  
Online Setting

The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting.

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