Relevant bibliographies by topics / Maritime anomaly detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Maritime anomaly detection'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Maritime anomaly detection"

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Iphar, Clément, Cyril Ray, and Aldo Napoli. "Data integrity assessment for maritime anomaly detection." Expert Systems with Applications 147 (June 2020): 113219. http://dx.doi.org/10.1016/j.eswa.2020.113219.

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Kim, Donghyun, Gian Antariksa, Melia Putri Handayani, Sangbong Lee, and Jihwan Lee. "Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data." Sensors 21, no. 15 (July 31, 2021): 5200. http://dx.doi.org/10.3390/s21155200.

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In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not explain why the model classifies specific data instances as an anomaly. This study combines explainable AI techniques with anomaly detection algorithm to overcome the limitation above. As an explainable AI method, this study adopts Shapley Additive exPlanations (SHAP), which is theoretically solid and compatible with any kind of machine learning algorithm. SHAP enables us to measure the marginal contribution of each sensor variable to an anomaly. Thus, one can easily specify which sensor is responsible for the specific anomaly. To illustrate our framework, the actual sensor stream obtained from the cargo vessel collected over 10 months was analyzed. In this analysis, we performed hierarchical clustering analysis with transformed SHAP values to interpret and group common anomaly patterns. We showed that anomaly interpretation and segmentation using SHAP value provides more useful interpretation compared to the case without using SHAP value.
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Tserpes, Konstantinos, Konstantinos Chatzikokolakis, Dimitris Zissis, Giannis Spiliopoulos, and Ioannis Kontopoulos. "Real-time maritime anomaly detection: detecting intentional AIS switch-off." International Journal of Big Data Intelligence 7, no. 2 (2020): 85. http://dx.doi.org/10.1504/ijbdi.2020.10029526.

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Kontopoulos, Ioannis, Konstantinos Chatzikokolakis, Dimitris Zissis, Konstantinos Tserpes, and Giannis Spiliopoulos. "Real-time maritime anomaly detection: detecting intentional AIS switch-off." International Journal of Big Data Intelligence 7, no. 2 (2020): 85. http://dx.doi.org/10.1504/ijbdi.2020.107375.

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Sithiravel, Rajiv, Bhashyam Balaji, Bradley Nelson, Michael Kenneth McDonald, Ratnasingham Tharmarasa, and Thiagalingam Kirubarajan. "Airborne Maritime Surveillance Using Magnetic Anomaly Detection Signature." IEEE Transactions on Aerospace and Electronic Systems 56, no. 5 (October 2020): 3476–90. http://dx.doi.org/10.1109/taes.2020.2973866.

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Kazemi, Samira, Shahrooz Abghari, Niklas Lavesson, Henric Johnson, and Peter Ryman. "Open data for anomaly detection in maritime surveillance." Expert Systems with Applications 40, no. 14 (October 2013): 5719–29. http://dx.doi.org/10.1016/j.eswa.2013.04.029.

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Han, X., C. Armenakis, and M. Jadidi. "DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 455–61. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-455-2020.

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Abstract. Today maritime transportation represents 90% of international trade volume and there are more than 50,000 vessels sailing the ocean every day. Therefore, reducing maritime transportation security risks by systematically modelling and surveillance should be of high priority in the maritime domain. By statistics, majority of maritime accidents are caused by human error due to fatigue or misjudgment. Auto-vessels equipped with autonomous and semi-autonomous systems can reduce the reliance on human’s intervention, thus make maritime navigation safer. This paper presents a clustering method for route planning and trajectory anomalies detection, which are the essential part of auto-vessel system design and development. In this paper, we present the development of an enhanced density-based spatial clustering (DBSCAN) method that can be applied on historical or real-time Automatic Identification System (AIS) data, so that vessel routes can be modelled, and the trajectories’ anomalies can be detected. The proposed methodology is based on developing an optimized trajectory clustering approach in two stages. Firstly, to increase the attribute dimension of the vessel’s positioning data, therefore other characteristics such as velocity and direction are considered in the clustering process along with geospatial information. Secondly, the DBSCAN clustering model has been enhanced by introducing the Mahalanobis Distance metric considering the correlations of the position cluster points aiming to make the identification process more accurate as well as reducing the computational cost.
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Michałowska, Katarzyna, Signe Riemer-Sørensen, Camilla Sterud, and Ole Magnus Hjellset. "Anomaly Detection with Unknown Anomalies: Application to Maritime Machinery." IFAC-PapersOnLine 54, no. 16 (2021): 105–11. http://dx.doi.org/10.1016/j.ifacol.2021.10.080.

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Park, Jaemin, and Sungil Kim. "Maritime Anomaly Detection Based on VAE-CUSUM Monitoring System." Journal of the Korean Institute of Industrial Engineers 46, no. 4 (August 31, 2020): 432–42. http://dx.doi.org/10.7232/jkiie.2020.46.4.432.

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Lei, Po-Ruey. "A framework for anomaly detection in maritime trajectory behavior." Knowledge and Information Systems 47, no. 1 (May 19, 2015): 189–214. http://dx.doi.org/10.1007/s10115-015-0845-4.

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Dissertations / Theses on the topic "Maritime anomaly detection"

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Riveiro, María José. "Visual analytics for maritime anomaly detection." Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-12783.

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The surveillance of large sea areas typically involves  the analysis of huge quantities of heterogeneous data.  In order to support the operator while monitoring maritime traffic, the identification of anomalous behavior or situations that might need further investigation may reduce operators' cognitive load. While it is worth acknowledging that existing mining applications support the identification of anomalies, autonomous anomaly detection systems are rarely used for maritime surveillance. Anomaly detection is normally a complex task that can hardly be solved by using purely visual or purely computational methods. This thesis suggests and investigates the adoption of visual analytics principles to support the detection of anomalous vessel behavior in maritime traffic data. This adoption involves studying the analytical reasoning process that needs to be supported,  using combined automatic and visualization approaches to support such process, and evaluating such integration. The analysis of data gathered during interviews and participant observations at various maritime control centers and the inspection of video recordings of real anomalous incidents lead to a characterization of the analytical reasoning process that operators go through when monitoring traffic. These results are complemented with a literature review of anomaly detection techniques applied to sea traffic. A particular statistical-based technique is implemented, tested, and embedded in a proof-of-concept prototype that allows user involvement in the detection process. The quantitative evaluation carried out by employing the prototype reveals that participants who used the visualization of normal behavioral models outperformed the group without aid. The qualitative assessment shows that  domain experts are positive towards providing automatic support and the visualization of normal behavioral models, since these aids may reduce reaction time, as well as increase trust and comprehensibility in the system. Based on the lessons learned, this thesis provides recommendations for designers and developers of maritime control and anomaly detection systems, as well as guidelines for carrying out evaluations of visual analytics environments.
Maria Riveiro is also affiliated to Informatics Research Centre, Högskolan i Skövde
Information Fusion Research Program, Högskolan i Skövde
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Abghari, Shahrooz, and Samira Kazemi. "Open Data for Anomaly Detection in Maritime Surveillance." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4807.

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Context: Maritime Surveillance (MS) has received increased attention from a civilian perspective in recent years. Anomaly detection (AD) is one of the many techniques available for improving the safety and security in the MS domain. Maritime authorities utilize various confidential data sources for monitoring the maritime activities; however, a paradigm shift on the Internet has created new sources of data for MS. These newly identified data sources, which provide publicly accessible data, are the open data sources. Taking advantage of the open data sources in addition to the traditional sources of data in the AD process will increase the accuracy of the MS systems. Objectives: The goal is to investigate the potential open data as a complementary resource for AD in the MS domain. To achieve this goal, the first step is to identify the applicable open data sources for AD. Then, a framework for AD based on the integration of open and closed data sources is proposed. Finally, according to the proposed framework, an AD system with the ability of using open data sources is developed and the accuracy of the system and the validity of its results are evaluated. Methods: In order to measure the system accuracy, an experiment is performed by means of a two stage random sampling on the vessel traffic data and the number of true/false positive and negative alarms in the system is verified. To evaluate the validity of the system results, the system is used for a period of time by the subject matter experts from the Swedish Coastguard. The experts check the detected anomalies against the available data at the Coastguard in order to obtain the number of true and false alarms. Results: The experimental outcomes indicate that the accuracy of the system is 99%. In addition, the Coastguard validation results show that among the evaluated anomalies, 64.47% are true alarms, 26.32% are false and 9.21% belong to the vessels that remain unchecked due to the lack of corresponding data in the Coastguard data sources. Conclusions: This thesis concludes that using open data as a complementary resource for detecting anomalous behavior in the MS domain is not only feasible but also will improve the efficiency of the surveillance systems by increasing the accuracy and covering some unseen aspects of maritime activities.
This thesis investigated the potential open data as a complementary resource for Anomaly Detection (AD) in the Maritime Surveillance (MS) domain. A framework for AD was proposed based on the usage of open data sources along with other traditional sources of data. According to the proposed AD framework and the algorithms for implementing the expert rules, the Open Data Anomaly Detection System (ODADS) was developed. To evaluate the accuracy of the system, an experiment on the vessel traffic data was conducted and an accuracy of 99% was obtained for the system. There was a false negative case in the system results that decreased the accuracy. It was due to incorrect AIS data in a special situation that was not possible to be handled by the detection rules in the scope of this thesis. The validity of the results was investigated by the subject matter experts from the Swedish Coastguard. The validation results showed that the majority of the ODADS evaluated anomalies were true alarms. Moreover, a potential information gap in the closed data sources was observed during the validation process. Despite the high number of true alarms, the number of false alarms was also considerable that was mainly because of the inaccurate open data. This thesis provided insights into the open data as a complement to the common data sources in the MS domain and is concluded that using open data will improve the efficiency of the surveillance systems by increasing the accuracy and covering some unseen aspects of maritime activities.
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Brax, Christoffer. "Anomaly detection in the surveillance domain." Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-16373.

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In the post September 11 era, the demand for security has increased in virtually all parts of the society. The need for increased security originates from the emergence of new threats which differ from the traditional ones in such a way that they cannot be easily defined and are sometimes unknown or hidden in the “noise” of daily life. When the threats are known and definable, methods based on situation recognition can be used find them. However, when the threats are hard or impossible to define, other approaches must be used. One such approach is data-driven anomaly detection, where a model of normalcy is built and used to find anomalies, that is, things that do not fit the normal model. Anomaly detection has been identified as one of many enabling technologies for increasing security in the society. In this thesis, the problem of how to detect anomalies in the surveillance domain is studied. This is done by a characterisation of the surveillance domain and a literature review that identifies a number of weaknesses in previous anomaly detection methods used in the surveillance domain. Examples of identified weaknesses include: the handling of contextual information, the inclusion of expert knowledge and the handling of joint attributes. Based on the findings from this study, a new anomaly detection method is proposed. The proposed method is evaluated with respect to detection performance and computational cost on a number datasets, recorded from real-world sensors, in different application areas of the surveillance domain. Additionally, the method is also compared to two other commonly used anomaly detection methods. Finally, the method is evaluated on a dataset with anomalies developed together with maritime subject matter experts. The conclusion of the thesis is that the proposed method has a number of strengths compared to previous methods and is suitable foruse in operative maritime command and control systems.
Christoffer Brax forskar också vid högskolan i Skövde, Informatics Research Centre / Christoffer Brax also does research at the University of Skövde, Informatics Research Centre
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Pellissier, Muriel. "Anomaly detection technique for sequential data." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM078/document.

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De nos jours, beaucoup de données peuvent être facilement accessibles. Mais toutes ces données ne sont pas utiles si nous ne savons pas les traiter efficacement et si nous ne savons pas extraire facilement les informations pertinentes à partir d'une grande quantité de données. Les techniques de détection d'anomalies sont utilisées par de nombreux domaines afin de traiter automatiquement les données. Les techniques de détection d'anomalies dépendent du domaine d'application, des données utilisées ainsi que du type d'anomalie à détecter.Pour cette étude nous nous intéressons seulement aux données séquentielles. Une séquence est une liste ordonnée d'objets. Pour de nombreux domaines, il est important de pouvoir identifier les irrégularités contenues dans des données séquentielles comme par exemple les séquences ADN, les commandes d'utilisateur, les transactions bancaires etc.Cette thèse présente une nouvelle approche qui identifie et analyse les irrégularités de données séquentielles. Cette technique de détection d'anomalies peut détecter les anomalies de données séquentielles dont l'ordre des objets dans les séquences est important ainsi que la position des objets dans les séquences. Les séquences sont définies comme anormales si une séquence est presque identique à une séquence qui est fréquente (normale). Les séquences anormales sont donc les séquences qui diffèrent légèrement des séquences qui sont fréquentes dans la base de données.Dans cette thèse nous avons appliqué cette technique à la surveillance maritime, mais cette technique peut être utilisée pour tous les domaines utilisant des données séquentielles. Pour notre application, la surveillance maritime, nous avons utilisé cette technique afin d'identifier les conteneurs suspects. En effet, de nos jours 90% du commerce mondial est transporté par conteneurs maritimes mais seulement 1 à 2% des conteneurs peuvent être physiquement contrôlés. Ce faible pourcentage est dû à un coût financier très élevé et au besoin trop important de ressources humaines pour le contrôle physique des conteneurs. De plus, le nombre de conteneurs voyageant par jours dans le monde ne cesse d'augmenter, il est donc nécessaire de développer des outils automatiques afin d'orienter le contrôle fait par les douanes afin d'éviter les activités illégales comme les fraudes, les quotas, les produits illégaux, ainsi que les trafics d'armes et de drogues. Pour identifier les conteneurs suspects nous comparons les trajets des conteneurs de notre base de données avec les trajets des conteneurs dits normaux. Les trajets normaux sont les trajets qui sont fréquents dans notre base de données.Notre technique est divisée en deux parties. La première partie consiste à détecter les séquences qui sont fréquentes dans la base de données. La seconde partie identifie les séquences de la base de données qui diffèrent légèrement des séquences qui sont fréquentes. Afin de définir une séquence comme normale ou anormale, nous calculons une distance entre une séquence qui est fréquente et une séquence aléatoire de la base de données. La distance est calculée avec une méthode qui utilise les différences qualitative et quantitative entre deux séquences
Nowadays, huge quantities of data can be easily accessible, but all these data are not useful if we do not know how to process them efficiently and how to extract easily relevant information from a large quantity of data. The anomaly detection techniques are used in many domains in order to help to process the data in an automated way. The anomaly detection techniques depend on the application domain, on the type of data, and on the type of anomaly.For this study we are interested only in sequential data. A sequence is an ordered list of items, also called events. Identifying irregularities in sequential data is essential for many application domains like DNA sequences, system calls, user commands, banking transactions etc.This thesis presents a new approach for identifying and analyzing irregularities in sequential data. This anomaly detection technique can detect anomalies in sequential data where the order of the items in the sequences is important. Moreover, our technique does not consider only the order of the events, but also the position of the events within the sequences. The sequences are spotted as anomalous if a sequence is quasi-identical to a usual behavior which means if the sequence is slightly different from a frequent (common) sequence. The differences between two sequences are based on the order of the events and their position in the sequence.In this thesis we applied this technique to the maritime surveillance, but this technique can be used by any other domains that use sequential data. For the maritime surveillance, some automated tools are needed in order to facilitate the targeting of suspicious containers that is performed by the customs. Indeed, nowadays 90% of the world trade is transported by containers and only 1-2% of the containers can be physically checked because of the high financial cost and the high human resources needed to control a container. As the number of containers travelling every day all around the world is really important, it is necessary to control the containers in order to avoid illegal activities like fraud, quota-related, illegal products, hidden activities, drug smuggling or arm smuggling. For the maritime domain, we can use this technique to identify suspicious containers by comparing the container trips from the data set with itineraries that are known to be normal (common). A container trip, also called itinerary, is an ordered list of actions that are done on containers at specific geographical positions. The different actions are: loading, transshipment, and discharging. For each action that is done on a container, we know the container ID and its geographical position (port ID).This technique is divided into two parts. The first part is to detect the common (most frequent) sequences of the data set. The second part is to identify those sequences that are slightly different from the common sequences using a distance-based method in order to classify a given sequence as normal or suspicious. The distance is calculated using a method that combines quantitative and qualitative differences between two sequences
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Laxhammar, Rikard. "Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications." Doctoral thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762.

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Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of anomalous trajectories in surveillance applications. The main results and contributions of the thesis are two-fold. Firstly, we propose and discuss a novel approach for anomaly detection, called conformal anomaly detection, which is based on conformal prediction (Vovk et al.). In particular, we propose two general algorithms for anomaly detection: the conformal anomaly detector (CAD) and the computationally more efficient inductive conformal anomaly detector (ICAD). A key property of conformal anomaly detection, in contrast to previous methods, is that it provides a well-founded approach for the tuning of the anomaly threshold that can be directly related to the expected or desired alarm rate. Secondly, we propose and analyse two parameter-light algorithms for unsupervised online learning and sequential detection of anomalous trajectories based on CAD and ICAD: the sequential Hausdorff nearest neighbours conformal anomaly detector (SHNN-CAD) and the sequential sub-trajectory local outlier inductive conformal anomaly detector (SSTLO-ICAD), which is more sensitive to local anomalous sub-trajectories. We implement the proposed algorithms and investigate their classification performance on a number of real and synthetic datasets from the video and maritime surveillance domains. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning on video trajectories. Moreover, we demonstrate that SSTLO-ICAD is able to accurately discriminate realistic anomalous vessel trajectories from normal background traffic.
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McAbee, Ashley S. M. "Traffic pattern detection using the Hough transformation for anomaly detection to improve maritime domain awareness." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/38977.

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Techniques for anomaly detection in the maritime domain by extracting traffic patterns from ship position data to generate atlases of expected ocean travel are developed in this thesis. An archive of historical data is used to develop a traffic density grid. The Hough transformation is used to extract linear patterns of elevated density from the traffic density grid, which can be considered the highways of the oceans. These highways collectively create an atlas that is used to define geographical regions of expected ship locations. Ship position reports are compared to the atlas of highways to flag as anomalous any ship that is not operating on an expected highway. The atlas generation techniques are demonstrated using automated information system (AIS) ship position data to detect highways in both open-ocean and coastal areas. Additionally, the atlas generation techniques are used to explore variability in ship traffic as a result of extreme weather and seasonal variation. Finally, anomaly detection is demonstrated by comparing AIS data from 2013 to the highways detected in the archive of data from 2012. The development of an automatic atlas generation technique that can be used to develop a definition of normal maritime behavior is the significant result of this thesis.
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Osekowska, Ewa. "Design and Implementation of a Maritime Traffic Modeling and Anomaly Detection Method." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00600.

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Nowadays ships are usually equipped with a system of marine instruments, one of which is an Automatic Identification System (AIS) transponder. The availability of the global AIS ship tracking data opened the possibilities to develop maritime security far beyond the simple collision prevention. The research work summarized in this thesis explores this opportunity, with the aim of developing an intuitive and comprehensible method for traffic modeling and anomaly detection in the maritime domain. The novelty of the method lays in employing the technique of artificial potential fields. The general idea is for the potentials to represent typical patterns of vessels' behaviors. A conflict between potentials, which have been observed in the past, and the potential of a vessel currently in motion, indicates an anomaly. The developed potential field based method has been examined using a web-based anomaly detection system STRAND (for Seafaring TRansport ANomaly Detection). Its applicability has been demonstrated in several publications, examining its scalability, modeling capabilities and detection performance. The experimental investigations led to identifying optimal detection resolution for different traffic areas (open sea, harbor and river), and extracting traffic rules, e.g., with regard to speed limits and course, i.e., right-hand sailing rule. The map-based display of modeled traffic patterns and detection cases has been analyzed as well, using several demonstrative cases. The massive AIS database created for this study, together with a dataset of real traffic incidents, provides an abundance of challenges for future studies.
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Nguyen, Van Duong. "Variational deep learning for time series modelling and analysis : applications to dynamical system identification and maritime traffic anomaly detection." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0227.

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Ce travail de thèse se focalise sur une classe de méthodes d’apprentissage profond, probabilistes et non-supervisées qui utilisent l’inférence variationnelle pour créer des modèles évolutifs de grande capacité pour ce type de données. Nous présentons deux classes d’apprentissage variationnel profond, puis nous les appliquons à deux problèmes spécifiques liés au domaine maritime. La première application est l’identification de systèmes dynamiques à partir de données bruitées et partiellement observées. Nous introduisons un cadre qui fusionne l’assimilation de données classique et l’apprentissage profond moderne pour retrouver les équations différentielles qui contrôlent la dynamique du système. En utilisant une formulation d’espace d’états, le cadre proposé intègre des composantes stochastiques pour tenir compte des variabilités stochastiques, des erreurs de modèle et des incertitudes de reconstruction. La deuxième application est la surveillance du trafic maritime à l’aide des données AIS. Nous proposons une architecture d’apprentissage profond probabiliste multitâche pouvant atteindre des performances très prometteuses dans différentes tâches liées à la surveillance du trafic maritime, telles que la reconstruction de trajectoire, l’identification du type de navire et la détection d’anomalie, tout en réduisant considérablement la quantité de données à stocker et le temps de calcul. temps. Pour la tâche la plus importante - la détection d’anomalie, nous introduisons un détecteur géospatialisé qui utilise l’apprentissage profond variationnel pour construire une représentation probabiliste des trajectoires AIS, puis détecter les anomalies en jugeant la probabilité de cette trajectoire
This thesis work focuses on a class of unsupervised, probabilistic deep learning methods that use variational inference to create high capacity, scalable models for time series modelling and analysis. We present two classes of variational deep learning, then apply them to two specific problems related to the maritime domain. The first application is the identification of dynamical systems from noisy and partially observed data. We introduce a framework that merges classical data assimilation and modern deep learning to retrieve the differential equations that control the dynamics of the system. Using a state space formulation, the proposed framework embeds stochastic components to account for stochastic variabilities, model errors and reconstruction uncertainties. The second application is maritime traffic surveillance using AIS data. We propose a multitask probabilistic deep learning architecture can achieve state-of-the-art performance in different maritime traffic surveillance related tasks, such as trajectory reconstruction, vessel type identification and anomaly detection, while reducing significantly the amount data to be stored and the calculation time. For the most important task—anomaly detection, we introduce a geospatial detector that uses variational deep learning to builds a probabilistic representation of AIS trajectories, then detect anomalies by judging how likely this trajectory is
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Erik, Bergenholtz. "False Alarm Reduction in Maritime Surveillance." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12655.

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Context. A large portion of all the transportation in the world consists of voyages over the sea. Systems such as Automatic Identification Systems (AIS) have been developed to aid in the surveillance of the maritime traffic, in order to help keeping the amount accidents and illegal activities down. In recent years a lot of time and effort has gone into automated surveillance of maritime traffic, with the purpose of finding and reporting behaviour deviating from what is considered normal. An issue with many of the present approaches is inaccuracy and the amount of false positives that follow from it. Objectives. This study continues the work presented by Woxberg and Grahn in 2015. In their work they used quadtrees to improve upon the existing tool STRAND, created by Osekowska et al. STRAND utilizes potential fields to build a model of normal behaviour from received AIS data, which can then be used to detect anomalies in the traffic. The goal of this study is to further improve the system by adding statistical analysis to reduce the number of false positives detected by Grahn and Woxberg's implementation. Method. The method for reducing false positives proposed in this thesis uses the charge in overlapping potential fields to approximate a normal distribution of the charge in the area. If a charge is too similar to that of the overlapping potential fields the detection is dismissed as a false positive. A series of experiments were ran to find out which of the methods proposed by the thesis are most suited for this application.   Results. The tested methods for estimating the normal distribution of a cell in the potential field, i.e. the unbiased formula for estimating the standard deviation and a version using Kalman filtering, both find as many of the confirmed anomalies as the base implementation, i.e. 9/12. Furthermore, both suggested methods reduce the amount of false positives by 11.5% in comparison to the base implementation, bringing the amount of false positives down to 17.7%. However, there are indications that the unbiased method has more promise. Conclusion. The two proposed methods both work as intended and both proposed methods perform equally. There are however indications that the unbiased method may be better despite the test results, but a new extended set of training data is needed to confirm or deny this. The two methods can only work if the examined overlapping potential fields are independent from each other, which means that the methods can not be applied to anomalies of the positional variety. Constructing a filter for these anomalies is left for future study.
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Helldin, Tove. "Explanation Methods for Bayesian Networks." Thesis, University of Skövde, School of Humanities and Informatics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3193.

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The international maritime industry is growing fast due to an increasing number of transportations over sea. In pace with this development, the maritime surveillance capacity must be expanded as well, in order to be able to handle the increasing numbers of hazardous cargo transports, attacks, piracy etc. In order to detect such events, anomaly detection methods and techniques can be used. Moreover, since surveillance systems process huge amounts of sensor data, anomaly detection techniques can be used to filter out or highlight interesting objects or situations to an operator. Making decisions upon large amounts of sensor data can be a challenging and demanding activity for the operator, not only due to the quantity of the data, but factors such as time pressure, high stress and uncertain information further aggravate the task. Bayesian networks can be used in order to detect anomalies in data and have, in contrast to many other opaque machine learning techniques, some important advantages. One of these advantages is the fact that it is possible for a user to understand and interpret the model, due to its graphical nature.

This thesis aims to investigate how the output from a Bayesian network can be explained to a user by first reviewing and presenting which methods exist and second, by making experiments. The experiments aim to investigate if two explanation methods can be used in order to give an explanation to the inferences made by a Bayesian network in order to support the operator’s situation awareness and decision making process when deployed in an anomaly detection problem in the maritime domain.

 

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Book chapters on the topic "Maritime anomaly detection"

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Wu, Ying, Anthony Patterson, Rafael D. C. Santos, and Nandamudi L. Vijaykumar. "Topology Preserving Mapping for Maritime Anomaly Detection." In Computational Science and Its Applications – ICCSA 2014, 313–26. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09153-2_24.

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Janssens, Jeroen, Eric Postma, and Jaap van den Herik. "Density-Based Anomaly Detection in the Maritime Domain." In Situation Awareness with Systems of Systems, 119–31. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6230-9_8.

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Hoque, Ximi, and Sudhir Kumar Sharma. "Ensembled Deep Learning Approach for Maritime Anomaly Detection System." In Proceedings of ICETIT 2019, 862–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30577-2_77.

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Thomopoulos, Stelios C. A., Constantinos Rizogannis, Konstantinos Georgios Thanos, Konstantinos Dimitros, Konstantinos Panou, and Dimitris Zacharakis. "OCULUS Sea™ Forensics: An Anomaly Detection Toolbox for Maritime Surveillance." In Business Information Systems Workshops, 485–95. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36691-9_41.

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Anneken, Mathias, Yvonne Fischer, and Jürgen Beyerer. "Quantitative Assessment of Anomaly Detection Algorithms in Annotated Datasets from the Maritime Domain." In Studies in Computational Intelligence, 89–107. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33386-1_5.

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Kontopoulos, Ioannis, Iraklis Varlamis, and Konstantinos Tserpes. "Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection." In Lecture Notes in Computer Science, 6–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38081-6_2.

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Nava, Jose, and Alejandro Osorio. "A Hybrid Intelligent Risk Identification Model for Configuration Management in Aerospace Systems." In Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations, 319–45. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9779-9.ch017.

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This chapter proposes a multi-dimensional patterns recognition model for Configuration Management's Risk Identification in Aerospace Safety Critical Systems. This work has been designed for Aerospace software systems where companies require full compliance with the Aerospace Standard DO-178b. The solution focuses on Risk Identification for the Configuration Management Process Area. An Anomaly Detection Solution has been designed through the modeling of statistics and artificial intelligence algorithms, following CRISP-DM model standard for data mining solutions. A dimensional architecture was designed to model the problem through three dependent and interconnected dimensions. The first dimension, Behavioral Biometrics, which this model has extended to Human Behavioral Patterns. The second dimension is Infrastructure, which represents all physical specialized equipment, environments, networking, and its configurations. The third dimension is space-time, which in this model represents a time dimension against all geographical information project related (code, files, among others).
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Conference papers on the topic "Maritime anomaly detection"

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Zor, C., and J. Kittler. "Maritime anomaly detection in ferry tracks." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952636.

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Lane, R. O., D. A. Nevell, S. D. Hayward, and T. W. Beaney. "Maritime anomaly detection and threat assessment." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711998.

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Roy, Jean. "Anomaly detection in the maritime domain." In SPIE Defense and Security Symposium, edited by Craig S. Halvorson, Daniel Lehrfeld, and Theodore T. Saito. SPIE, 2008. http://dx.doi.org/10.1117/12.776230.

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Anneken, Mathias, Anne-Laure Jousselme, Sebastian Robert, and Jurgen Beyerer. "Synthetic Trajectory Extraction for Maritime Anomaly Detection." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00204.

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Chatzikokolakis, Konstantinos, Dimitris Zissis, Marios Vodas, Giannis Spiliopoulos, and Ioannis Kontopoulos. "A distributed lightning fast maritime anomaly detection service." In OCEANS 2019 - Marseille. IEEE, 2019. http://dx.doi.org/10.1109/oceanse.2019.8867269.

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Roy, Jean. "Rule-based expert system for maritime anomaly detection." In SPIE Defense, Security, and Sensing, edited by Edward M. Carapezza. SPIE, 2010. http://dx.doi.org/10.1117/12.849131.

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Murray, Brian, and Lokukaluge P. Perera. "Unsupervised Trajectory Anomaly Detection for Situation Awareness in Maritime Navigation." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18281.

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Abstract Situation awareness is essential in conducting effective collision avoidance in potential ship encounter situations. It has been shown that data driven trajectory prediction techniques, utilizing historical AIS data, have the potential to aid in providing such awareness. However, such data driven techniques will not perform well for unusual ship behavior, i.e. anomalous trajectories. Additionally, such anomalies in the dataset can corrupt the predictions. In this study, an unsupervised approach to anomaly detection is presented to aid such trajectory predictions. Gaussian Mixture Models are used to cluster trajectories, such that clusters of both normal and anomalous trajectories are discovered. Further, anomalies are discovered within clusters of normal behavior. Novel trajectories can then also be evaluated based on a parametric description of the historical ship traffic. The approach is shown to be effective in detecting anomalies relevant in such a trajectory prediction scheme.
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Avram, Vladimir, Uwe Glasser, and Hamed Yaghoubi Shahir. "Anomaly detection in spatiotemporal data in the maritime domain." In 2012 IEEE International Conference on Intelligence and Security Informatics (ISI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isi.2012.6284274.

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Vespe, M., I. Visentini, K. Bryan, and P. Braca. "Unsupervised learning of maritime traffic patterns for anomaly detection." In 9th IET Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications. IET, 2012. http://dx.doi.org/10.1049/cp.2012.0414.

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Vandecasteele, Arnaud, and Aldo Napoli. "An enhanced spatial reasoning ontology for maritime anomaly detection." In 2012 7th International Conference on System of Systems Engineering (SoSE). IEEE, 2012. http://dx.doi.org/10.1109/sysose.2012.6384120.

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