scholarly journals A Unified, Clustering-Based Framework for Detection of Spatial and Energy Anomalies in Trajectories Utilizing ADS-B Data

2021 ◽  
Author(s):  
Samantha Corrado ◽  
Tejas Puranik ◽  
Olivia Pinon Fischer ◽  
Dimitri Mavris
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Air Traffic ◽  
Data Driven ◽  
System Level ◽  
Detection Methods ◽  
Trajectory Data ◽  
Traffic Demand ◽  
Energy Behavior ◽  
Standard Operations

As air traffic demand grows, robust, data-driven anomaly detection methods are required to ensure that aviation systems become safer and more efficient. The terminal airspace is identified as the most critical airspace for both individual flight-level and system-level safety and efficiency. As such, developing data-driven anomaly detection methods to analyze terminal airspace operations is paramount. With the expansion of ADS-B technology, open-source flight tracking data has become more readily available to enable larger-scale analyses of aircraft operations. This paper makes a distinction between spatial metrics in ADS-B trajectory data and energy metrics derived from ADS-B trajectory data. Motivated by the limited number of approaches that simultaneously consider both spatial and energy metrics, this paper introduces the concepts of spatial anomalies and energy anomalies. In particular, it proposes a novel, unified framework for detection of spatial and energy anomalies in ADS-B trajectory data (and associated derived metrics). The framework consists of three main parts - a data processing procedure, a spatial anomaly detection method, and an energy anomaly detection method. The framework is demonstrated utilizing four months of ADS-B trajectory data associated with arrivals at San Francisco International Airport, and the relationship between the spatial and energy anomalies in this terminal airspace is explored. The results that stem from the implementation of this framework indicate that if an aircraft is spatially not conforming to an identified set of air traffic flows representing standard spatial operations, then this aircraft is more likely to experience non-conformance to standard operations in its energy metrics. Aviation operators, such as air traffic controllers, may benefit from this observation, as it may factor into decision-making in instances where there is the potential to instruct an aircraft to spatially deviate from standard operations. Additionally, this research revealed underlying differences between trajectories that are spatially nominal yet energy-anomalous and those trajectories that are spatially anomalous and energy-anomalous. Focusing solely on energy anomaly detection does not provide insight into potential spatial-related decisions that may have been made to result in off-nominal energy behavior.

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 Recent Advances in Anomaly Detection Methods Applied to Aviation

Aerospace ◽  
2019 ◽  
Vol 6 (11) ◽  
pp. 117 ◽  
Author(s):  
Luis Basora ◽  
Xavier Olive ◽  
Thomas Dubot
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Advances ◽  
Flight Operations

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

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 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.

Rotor health monitoring combining spin tests and data-driven anomaly detection methods

2011 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Ali Abdul-Aziz ◽  
Mark R Woike ◽  
Nikunj C Oza ◽  
Bryan L Matthews ◽  
John D lekki
Keyword(s):  
Anomaly Detection ◽  
Health Monitoring ◽  
Data Driven ◽  
Detection Methods

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.

A Structural Evolution-Based Anomaly Detection Method for Generalized Evolving Social Networks

2020 ◽  
Author(s):  
Huan Wang ◽  
Qing Gao ◽  
Hao Li ◽  
Hao Wang ◽  
Liping Yan ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Anomaly Detection ◽  
Detection Method ◽  
Structural Information ◽  
Structural Evolution ◽  
The State ◽  
Detection Methods ◽  
State Change ◽  
State Construction

Abstract Recently, text-based anomaly detection methods have obtained impressive results in social network services, but their applications are limited to social texts provided by users. To propose a method for generalized evolving social networks that have limited structural information, this study proposes a novel structural evolution-based anomaly detection method ($SeaDM$), which mainly consists of an evolutional state construction algorithm ($ESCA$) and an optimized evolutional observation algorithm ($OEOA$). $ESCA$ characterizes the structural evolution of the evolving social network and constructs the evolutional state to represent the macroscopic evolution of the evolving social network. Subsequently, $OEOA$ reconstructs the quantum-inspired genetic algorithm to discover the optimized observation vector of the evolutional state, which maximally reflects the state change of the evolving social network. Finally, $SeaDM$ combines $ESCA$ and $OEOA$ to evaluate the state change degrees and detect anomalous changes to report anomalies. Experimental results on real-world evolving social networks with artificial and real anomalies show that our proposed $SeaDM$ outperforms the state-of-the-art anomaly detection methods.

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