Photonic Reservoir Computing To Detect Anomalies And Intrusions In Optical Networks
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Photonic Reservoir Computing To Detect Anomalies And Intrusions In Optical Networks
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Author : Josep Torné Chertó
language : en
Publisher:
Release Date : 2020
Photonic Reservoir Computing To Detect Anomalies And Intrusions In Optical Networks written by Josep Torné Chertó and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Optical networks are a critical infrastructure subjected to attacks at its physical layer. Different methods have been presented to prevent, detect and solve these attacks. The current systems in charge of detecting intrusions in optical networks rely on the metrics obtained from OPM equipment. This equipment has a high cost, making the massive deployment through the optical network unfeasible. Photonic reservoir computing is a technology that has been able to solve tasks as header recognition, channel equalization, etc. with a good performance and a cost that allows it to be deployed over all the network. In this thesis it is designed and assessed by simulation a photonic reservoir computing system able to detect and classify attacks in optical networks.
Underground Sensing
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Author : Sibel Pamukcu
language : en
Publisher: Academic Press
Release Date : 2017-10-22
Underground Sensing written by Sibel Pamukcu and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-22 with Technology & Engineering categories.
Underground Sensing: Monitoring and Hazard Detection for Environment and Infrastructure brings the target audience the technical and practical knowledge of existing technologies of subsurface sensing and monitoring based on a classification of their functionality. In addition, the book introduces emerging technologies and applications of sensing for environmental and geo-hazards in subsurface – focusing on sensing platforms that can enable fully distributed global measurements. Finally, users will find a comprehensive exploration of the future of underground sensing that can meet demands for preemptive and sustainable response to underground hazards. New concepts and paradigms based on passively powered and/or on-demand activated, embeddable sensor platforms are presented to bridge the gap between real-time monitoring and global measurements. - Presents a one-stop-shop reference for underground sensing and monitoring needs that saves valuable research time - Provides application cases for all technologies that are covered and described in detail - Includes full, four color images of equipment and applications - Designed to cover a wide variety of underground sensors, from agriculture to geohazards
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Machine Learning Techniques For Multimedia
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Author : Matthieu Cord
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-02-07
Machine Learning Techniques For Multimedia written by Matthieu Cord and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-07 with Computers categories.
Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying machine learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with machine learning in multimedia applications.
An Introduction To Distributed Optical Fibre Sensors
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Author : Arthur H. Hartog
language : en
Publisher: CRC Press
Release Date : 2017-05-25
An Introduction To Distributed Optical Fibre Sensors written by Arthur H. Hartog and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-25 with Science categories.
This book explains physical principles, unique benefits, broad categories, implementation aspects, and performance criteria of distributed optical fiber sensors (DOFS). For each kind of sensor, the book highlights industrial applications, which range from oil and gas production to power line monitoring, plant and process engineering, environmental monitoring, industrial fire and leakage detection, and so on. The text also includes a discussion of such key areas as backscattering, launched power limitations, and receiver sensitivity, as well as a concise historical account of the field’s development.
Body Sensor Networks
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Author : Guang-Zhong Yang
language : en
Publisher: Springer
Release Date : 2014-04-16
Body Sensor Networks written by Guang-Zhong Yang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-16 with Computers categories.
The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for wellbeing and healthcare. One key development in this area is wireless, wearable and implantable in vivo monitoring and intervention. A myriad of platforms are now available from both academic institutions and commercial organisations. They permit the management of patients with both acute and chronic symptoms, including diabetes, cardiovascular diseases, treatment of epilepsy and other debilitating neurological disorders. Despite extensive developments in sensing technologies, there are significant research issues related to system integration, sensor miniaturisation, low-power sensor interface, wireless telemetry and signal processing. In the 2nd edition of this popular and authoritative reference on Body Sensor Networks (BSN), major topics related to the latest technological developments and potential clinical applications are discussed, with contents covering. Biosensor Design, Interfacing and Nanotechnology Wireless Communication and Network Topologies Communication Protocols and Standards Energy Harvesting and Power Delivery Ultra-low Power Bio-inspired Processing Multi-sensor Fusion and Context Aware Sensing Autonomic Sensing Wearable, Ingestible Sensor Integration and Exemplar Applications System Integration and Wireless Sensor Microsystems The book also provides a comprehensive review of the current wireless sensor development platforms and a step-by-step guide to developing your own BSN applications through the use of the BSN development kit.
Anomaly Detection Through Statistics Based Machine Learning For Computer Networks
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Author :
language : en
Publisher:
Release Date : 2006
Anomaly Detection Through Statistics Based Machine Learning For Computer Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computer networks categories.
The intrusion detection in computer networks is a complex research problem, which requires the understanding of computer networks and the mechanism of intrusions, the configuration of sensors and the collected data, the selection of the relevant attributes, and the monitor algorithms for online detection. It is critical to develop general methods for data dimension reduction, effective monitoring algorithms for intrusion detection, and means for their performance improvement. This dissertation is motivated by the timely need to develop statistics-based machine learning methods for effective detection of computer network anomalies. Three fundamental research issues related to data dimension reduction, control charts design and performance improvement have been addressed accordingly. The major research activities and corresponding contributions are summarized as follows:(1) Filter and Wrapper models are integrated to extract a small number of the informative attributes for computer network intrusion detection. A two-phase analyses method is proposed for the integration of Filter and Wrapper models. The proposed method has successfully reduced the original 41 attributes to 12 informative attributes while increasing the accuracy of the model. The comparison of the results in each phase shows the effectiveness of the proposed method.(2) Supervised kernel based control charts for anomaly intrusion detection. We propose to construct control charts in a feature space. The first contribution is the use of multi-objective Genetic Algorithm in the parameter pre-selection for SVM based control charts. The second contribution is the performance evaluation of supervised kernel based control charts.(3) Unsupervised kernel based control charts for anomaly intrusion detection. Two types of unsupervised kernel based control charts are investigated: Kernel PCA control charts and Support Vector Clustering based control charts. The applications of SVC based control charts on computer networks audit data are also discussed to demonstrate the effectiveness of the proposed method. Although the developed methodologies in this dissertation are demonstrated in the computer network intrusion detection applications, the methodologies are also expected to be applied to other complex system monitoring, where the database consists of a large dimensional data with non-Gaussian distribution.
A Grid Based Anomaly Detection Approach For Intrusion Detection In Computer Network Systems
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Author : Erica Ross Turnbull
language : en
Publisher:
Release Date : 2003
A Grid Based Anomaly Detection Approach For Intrusion Detection In Computer Network Systems written by Erica Ross Turnbull and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computer networks categories.
Characterization And Development Of An Optical Intrusion Detection System For Current And Next Generation Of Optical Networks
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Author : Abrar Salim
language : en
Publisher:
Release Date : 2011
Characterization And Development Of An Optical Intrusion Detection System For Current And Next Generation Of Optical Networks written by Abrar Salim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Electronic alarm systems categories.
Network Based Intrusion Detection System With Anomaly Detection
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Author : WONG WEI HAN (TP025331)
language : en
Publisher:
Release Date : 2013
Network Based Intrusion Detection System With Anomaly Detection written by WONG WEI HAN (TP025331) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.