# Recent Advances in Anomaly Detection Methods Applied to Aviation

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## Abstract

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## 1. Introduction

#### 1.1. Anomaly Detection

- Point anomalies. A data point that differs significantly from the rest of the data points in the dataset considered. For instance, in a time series of French temperatures in summer, a temperature of $40{\phantom{\rule{3.33333pt}{0ex}}}^{\circ}$C can be considered as an anomaly even with the undergoing climate change.
- Contextual anomalies. When a data point is an anomaly only in a particular context. The context is defined by the contextual attributes, which usually refer to time (time series) or location. For instance, in a time series of summer temperatures by country, a temperature of $40{\phantom{\rule{3.33333pt}{0ex}}}^{\circ}$C is an anomaly in France, but it might be not in hotter countries like Libya where temperatures in summer are commonly around $40{\phantom{\rule{3.33333pt}{0ex}}}^{\circ}$C. Attributes (e.g., temperature) indexed by contextual attributes (e.g., country) are called behavioural attributes. Not only anomalies in spatial data but also in time series fall into this category, e.g., $40{\phantom{\rule{3.33333pt}{0ex}}}^{\circ}$C can be an anomaly in Libya from October to April, as at this time average temperatures range from $15{\phantom{\rule{3.33333pt}{0ex}}}^{\circ}$C to $30{\phantom{\rule{3.33333pt}{0ex}}}^{\circ}$C.
- Collective anomalies. When a group of data in a dataset is an anomaly as a whole, but the individual instances in that group (or subsets of them) might be not on their own. In time series, this would correspond for instance to a situation or condition persisting over an abnormal long time. Collective anomalies can only be detected in datasets where data is related someway, i.e., sequential, spatial or graph data.

#### 1.2. Previous Surveys on Anomaly Detection

#### 1.3. Motivation and Organisation of the Survey

## 2. Taxonomy of Classical Methods in Previous Surveys

#### 2.1. Distance-Based Methods

#### 2.1.1. Nearest Neighbour-Based Methods

#### 2.1.2. Clustering-Based Methods

- In the first category, the techniques assume that normal data instances belong to a cluster whereas anomalies do not: anomalies correspond instead to the so called clustering outliers or noise. Thus, any clustering algorithm that does not force all data instances to belong to a cluster can be used. The most popular ones are density-based clustering algorithms such as DBSCAN [23], HDBSCAN [24] or OPTICS [25].
- In the second category, the assumption is that normal instances are near their closest cluster centroid whereas anomalies lie far away from them. In this case, two steps are required for anomaly detection: run an algorithm to cluster the data, and then compute an anomaly score for each data instance based on the distance to its closest cluster centroid. An example of technique in this category often cited in aerospace papers is the Inductive Monitoring System (IMS) algorithm [26].
- The third category addresses the issue with the methods in the two previous categories when clusters of anomalies are formed. This is because the assumption is now that normal instances belong to large and dense clusters and anomalies to sparse or small clusters. A threshold is thus defined on the cluster size or density to determine the anomaly cases.

#### 2.2. Ensemble-Based Methods

#### 2.3. Statistical Methods

#### 2.3.1. Gaussian Mixture Models

#### 2.3.2. Independent Component Analysis

#### 2.3.3. Regression Model-Based

#### 2.4. Domain-Based Methods

#### 2.5. Reconstruction-Based Methods

#### 2.5.1. Subspace-Based Methods

#### 2.5.2. Neural Network Methods

## 3. Recent Advances in Anomaly Detection

#### 3.1. Recent Advances in Recurrent Neural Networks

#### 3.2. Recent Advances in Convolutional Neural Networks

#### 3.3. Recent Advances in Autoencoders

#### 3.4. Recent Advances in Generative Models

#### 3.5. Recent Advances in Temporal Logic-Based Learning

## 4. Applications

#### 4.1. Anomaly Detection for Air Traffic Operations

#### 4.1.1. Domain-Based Approaches

#### 4.1.2. Distance-Based Approaches

#### 4.1.3. Reconstruction-Based Approaches

#### 4.1.4. Statistical-Based Approaches

#### 4.1.5. Temporal-Logic Learning Based Approaches

#### 4.2. Anomaly Detection for Predictive Maintenance Operations in Aviation

- NASA DASHlink open database originally designed and collected by Balaban [118] and available at https://c3.nasa.gov/dashlink/projects/85/;
- a turbofan engine degradation simulation dataset based on thermo-dynamical simulation models, introduced in [119];
- other datasets, also shared on the Prognostic Data Repository of NASA refer to bearing systems or milling machines. These do not necessarily refer to aviation problems but are still worth mentioning as they are commonly used as reference.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

AE | Autoencoder |

ARIMA | Auto Regressive Integrated Moving Averages |

CNN | Convolutional Neural Network |

DAE | Deep Autoencoder |

DBN | Deep Belief Network |

DBSCAN | Density-Based Spatial Clustering of Applications with Noise |

ELM | Extreme Learning Machines |

GAN | Generative Adversarial Network |

GLOSH | Global-Local Outlier Score from Hierarchies |

GMM | Gaussian Mixture Model |

GRU | Gated Recurrent Unit |

ICA | Independent Component Analysis |

IF | Isolation Forest |

KDE | Kernel Density Estimation |

kNN | K-Nearest Neighbours |

IMF | Inductive Monitoring System |

LOF | Local Outlier Factor |

LoOP | Local Outlier Probability |

LSTM | Long Short-Term Memory |

MKAD | Multiple Kernel Anomaly Detection |

NN | Neural Network |

OC-SVM | One-Class Support Vector Machine |

OPTICS | Ordering Points To Identify the Clustering Structure |

PCA | Principal Component Analysis |

RBM | Riemann Boltzmann Machine |

RNN | Recurrent Neural Network |

STORN | Stochastic Recurrent Network |

SVM | Support Vector Machine |

VAE | Variational Autoencoder |

VAR | Vector Auto-Regressive |

ACARS | Aircraft Communication Addressing and Reporting System |

ADS-B | Automatic Dependent Surveillance–Broadcast |

ASIAS | Aviation Safety Information Analysis and Sharing |

ATC | Air Traffic Control |

ATM | Air Traffic Management |

CBM | Condition Based Maintenance |

FDM | Flight Data Monitoring |

FOQA | Flight Operations Quality Assurance |

NAS | National Airspace System |

PHM | Prognostics and Health Management |

RUL | Remained Useful Life |

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Recurrent Neural Networks | Section 3.1 | Stacked LSTM: [57] (2015) |

LSTM and GRU: [58] (2016) | ||

Hybrid LSTM with OC-SVM or SVDD: [59] (2017) | ||

Convolutional Neural Networks | Section 3.2 | Intrusion detection: [60] (2017), [61] (2017) |

Comparative study with other NN: [62] (2018) | ||

Advanced Autoencoders | Section 3.3 | LSTM-ED: [63] (2016) |

MSCRED: [64] (2018) | ||

Multi-modal DAE: [65] (2016) | ||

ConvLSTM-AE: [66] (2017) | ||

Generative Models | Section 3.4 | GAN: [33,67,68] (2018) |

Variational Inference: [69] (2016), [70] (2018) | ||

Temporal-logic Learning Models | Section 3.5 | Supervised model: [71] (2014) |

Unsupervised model: [72] (2014) | ||

Online model: [73] (2016) |

Section 4.1.1 Domain-based | Abnormal approaches with MKAD: [104] (2011) |

GA approach and landing anomalies with OC-SVM: [105] (2017) | |

Section 4.1.2 Distance-based | Anomalous pilot switching with SequenceMiner: [18] (2008) |

Anomalous take-off and approach operations: [19] (2011), [20] (2015) | |

Anomalous safety events with LoOP: [16] (2019) | |

Anomalous taxi paths with hierarchical clustering: [22] (2019) | |

Anomalous radiotelephony readbacks with kNN: [106] (2018) | |

Section 4.1.3 Reconstruction-based | Atypical aviation safety data with KPCA: [107] (2017) |

Atypical approaches and landings with FPCA: [52] (2018) | |

Anomalous trajectories in TMA and en-route: [108] (2018), [109] (2019) | |

Anomalous transitions between sector configurations: [110] (2018) | |

Anomalous ADS-B messages with ConvLSTM-AE: [86] (2019) | |

Section 4.1.4 Statistical-based | Anomalous flights with VARX: [38] (2016), |

Anomalous flight switches with VAR: [39] (2016) | |

Abnormal flight data with GMM: [21] (2016) | |

Anomalous air traffic congestion with ICA: [37] (2019) | |

Section 4.1.5 Temporal-logic based | Anomalous trajectories in terminal airspace with TempAD: [103,111] (2019) |

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**MDPI and ACS Style**

Basora, L.; Olive, X.; Dubot, T.
Recent Advances in Anomaly Detection Methods Applied to Aviation. *Aerospace* **2019**, *6*, 117.
https://doi.org/10.3390/aerospace6110117

**AMA Style**

Basora L, Olive X, Dubot T.
Recent Advances in Anomaly Detection Methods Applied to Aviation. *Aerospace*. 2019; 6(11):117.
https://doi.org/10.3390/aerospace6110117

**Chicago/Turabian Style**

Basora, Luis, Xavier Olive, and Thomas Dubot.
2019. "Recent Advances in Anomaly Detection Methods Applied to Aviation" *Aerospace* 6, no. 11: 117.
https://doi.org/10.3390/aerospace6110117