Distributed Anomaly Detection Using Minimum Volume Elliptical Principal Component Analysis

Principal component analysis and the residual error is an effective anomaly detection technique. In an environment where anomalies are present in the training set, the derived principal components can be skewed by the anomalies. A further aspect of anomaly detection is that data might be distributed across different nodes in a network and their communication to a centralized processing unit is prohibited due to communication cost. Current solutions to distributed anomaly detection rely on a hierarchical network infrastructure to aggregate data or models; however, in this environment, links close to the root of the tree become critical and congested. In this paper, an algorithm is proposed that is more robust in its derivation of the principal components of a training set containing anomalies. A distributed form of the algorithm is then derived where each node in a network can iterate towards the centralized solution by exchanging small matrices with neighboring nodes. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to principal component analysis and alternative anomaly detection techniques. In addition, it is shown that in a variety of network infrastructures, the distributed form of the anomaly detection model is able to derive a close approximation of the centralized model

Security analysis of network anomalies mitigation schemes in IoT networks

The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed

Exploring space situational awareness using neuromorphic event-based cameras

The orbits around earth are a limited natural resource and one that hosts a vast range of vital space-based systems that support international systems use by both commercial industries, civil organisations, and national defence. The availability of this space resource is rapidly depleting due to the ever-growing presence of space debris and rampant overcrowding, especially in the limited and highly desirable slots in geosynchronous orbit. The field of Space Situational Awareness encompasses tasks aimed at mitigating these hazards to on-orbit systems through the monitoring of satellite traffic. Essential to this task is the collection of accurate and timely observation data. This thesis explores the use of a novel sensor paradigm to optically collect and process sensor data to enhance and improve space situational awareness tasks. Solving this issue is critical to ensure that we can continue to utilise the space environment in a sustainable way. However, these tasks pose significant engineering challenges that involve the detection and characterisation of faint, highly distant, and high-speed targets. Recent advances in neuromorphic engineering have led to the availability of high-quality neuromorphic event-based cameras that provide a promising alternative to the conventional cameras used in space imaging. These cameras offer the potential to improve the capabilities of existing space tracking systems and have been shown to detect and track satellites or ‘Resident Space Objects’ at low data rates, high temporal resolutions, and in conditions typically unsuitable for conventional optical cameras. This thesis presents a thorough exploration of neuromorphic event-based cameras for space situational awareness tasks and establishes a rigorous foundation for event-based space imaging. The work conducted in this project demonstrates how to enable event-based space imaging systems that serve the goals of space situational awareness by providing accurate and timely information on the space domain. By developing and implementing event-based processing techniques, the asynchronous operation, high temporal resolution, and dynamic range of these novel sensors are leveraged to provide low latency target acquisition and rapid reaction to challenging satellite tracking scenarios. The algorithms and experiments developed in this thesis successfully study the properties and trade-offs of event-based space imaging and provide comparisons with traditional observing methods and conventional frame-based sensors. The outcomes of this thesis demonstrate the viability of event-based cameras for use in tracking and space imaging tasks and therefore contribute to the growing efforts of the international space situational awareness community and the development of the event-based technology in astronomy and space science applications

IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis

Beyond 100: The Next Century in Geodesy

This open access book contains 30 peer-reviewed papers based on presentations at the 27th General Assembly of the International Union of Geodesy and Geophysics (IUGG). The meeting was held from July 8 to 18, 2019 in Montreal, Canada, with the theme being the celebration of the centennial of the establishment of the IUGG. The centennial was also a good opportunity to look forward to the next century, as reflected in the title of this volume. The papers in this volume represent a cross-section of present activity in geodesy, and highlight the future directions in the field as we begin the second century of the IUGG. During the meeting, the International Association of Geodesy (IAG) organized one Union Symposium, 6 IAG Symposia, 7 Joint Symposia with other associations, and 20 business meetings. In addition, IAG co-sponsored 8 Union Symposia and 15 Joint Symposia. In total, 3952 participants registered, 437 of them with IAG priority. In total, there were 234 symposia and 18 Workshops with 4580 presentations, of which 469 were in IAG-associated symposia. ; This volume will publish papers based on International Association of Geodesy (IAG) -related presentations made at the International Association of Geodesy at the 27th IUGG General Assembly, Montreal, July 2019. It will include papers associated with all of the IAG and joint symposia from the meeting, which span all aspects of modern geodesy, and linkages to earth and environmental sciences. It continues the long-running IAG Symposia Series

Self-adaptive multi-agent systems for aided decision-making : an application to maritime surveillance

L'activité maritime s'est fortement développée ces dernières années et sert de support à de nombreuses activités illicites. Il est devenu nécessaire que les organismes impliqués dans la surveillance maritime disposent de systèmes efficaces pour les aider à identifier ces activités illicites. Les Systèmes de Surveillance Maritime doivent observer de manière efficace un espace maritime large, à identifier des anomalies de comportement des navires évoluant dans l'espace en question, et à déclencher des alertes documentées si ces anomalies amènent à penser que les navires ont un comportement suspect. Nous proposons un modèle générique de système multi-agents, que nous appelons MAS4AT, capable de remplir deux des différents rôles d'un système de surveillance : la représentation numérique des comportements des entités surveillées et des mécanismes d'apprentissage pour une meilleure efficacité. MAS4AT est intégré au système I2C.The maritime activity has widely grow in the last few years and is the witness of several illegal activities. It has become necessary that the organizations involved in the maritime surveillance possess efficient systems to help them in their identification. The maritime surveillance systems must observe a wide maritime area, identify the anomalies in the behaviours of the monitored ships et trigger alerts when these anomalies leads to a suspicious behavior. We propose a generic agent model, called MAS4AT, able to fulfil two main roles of a surveillance system: the numerical representation of the behaviours of the monitored entities and learning mechanisms for a better efficiency. MAS4AT is integrated in the system I2C

Beyond 100: The Next Century in Geodesy

This open access book contains 30 peer-reviewed papers based on presentations at the 27th General Assembly of the International Union of Geodesy and Geophysics (IUGG). The meeting was held from July 8 to 18, 2019 in Montreal, Canada, with the theme being the celebration of the centennial of the establishment of the IUGG. The centennial was also a good opportunity to look forward to the next century, as reflected in the title of this volume. The papers in this volume represent a cross-section of present activity in geodesy, and highlight the future directions in the field as we begin the second century of the IUGG. During the meeting, the International Association of Geodesy (IAG) organized one Union Symposium, 6 IAG Symposia, 7 Joint Symposia with other associations, and 20 business meetings. In addition, IAG co-sponsored 8 Union Symposia and 15 Joint Symposia. In total, 3952 participants registered, 437 of them with IAG priority. In total, there were 234 symposia and 18 Workshops with 4580 presentations, of which 469 were in IAG-associated symposia. ; This volume will publish papers based on International Association of Geodesy (IAG) -related presentations made at the International Association of Geodesy at the 27th IUGG General Assembly, Montreal, July 2019. It will include papers associated with all of the IAG and joint symposia from the meeting, which span all aspects of modern geodesy, and linkages to earth and environmental sciences. It continues the long-running IAG Symposia Series

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience