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Author : Antonio C. Cansado
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (796 download)
Download or read book Unsupervised Anomaly Detection Using Bayesian Networks and Gaussian Mixture Models written by Antonio C. Cansado and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Yogesh Pawar
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)
Download or read book A Study on Anomaly Detection Using Mixture Models written by Yogesh Pawar and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increase in networks capacities and number of online users, threats of different cyber attacks on computer networks also increased significantly, causing the loss of a vast amount of money every year to various organizations. This requires the need to identify and group these threats according to different attack types. Many anomaly detection systems have been introduced over the years based on different machine learning algorithms. More precisely, unsupervised learning algorithms have proven to be very effective. In many research studies, to build an effective ADS system, finite mixture models have been widely accepted as an essential clustering method. In this thesis, we deploy different non-Gaussian mixture models that have been proven to model well bounded and semi-bounded data. These models are based on the Dirichlet family of distributions. The deployed models are tested with Geometric Area Analysis Technique (GAA) and with an adversarial learning framework. Moreover, we build an effective hybrid anomaly detection system with finite and in-finite mixture models. In addition, we propose a feature selection approach based on the highest vote obtained. We evaluated the performance of mixture models with Geometric Area Analysis technique based on Trapezoidal Area Estimation (TAE) and the effect of adversarial learning on ADS performance via extensive experiments based on well-known data sets.
Author : Dhruba Kumar Bhattacharyya
Publisher : CRC Press
ISBN 13 : 1466582081
Total Pages : 368 pages
Book Rating : 4.4/5 (665 download)
Download or read book Network Anomaly Detection written by Dhruba Kumar Bhattacharyya and published by CRC Press. This book was released on 2013-06-18 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you’ll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.
Author : Scott Davies
Publisher :
ISBN 13 :
Total Pages : 181 pages
Book Rating : 4.:/5 (51 download)
Download or read book Fast Factored Density Estimation and Compression with Bayesian Networks written by Scott Davies and published by . This book was released on 2002 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Many important data analysis tasks can be addressed by formulating them as probability estimation problems. For example, a popular general approach to automatic classification problems is to learn a probabilistic model of each class from data in which the classes are known, and then use Bayes's rule with these models to predict the correct classes of other data for which they are not known. Anomaly detection and scientific discovery tasks can often be addressed by learning probability models over possible events and then looking for events to which these models assign low probabilities. Many data compression algorithms such as Huffman coding and arithmetic coding rely on probabilistic models of the data stream in order [sic] achieve high compression rates. In this thesis we examine several aspects of probability estimation algorithms. In particular, we focus on the automatic learning and use of probability models based on Bayesian networks, a convenient formalism in which the probability estimation task is split into many simpler subtasks. We also emphasize computational efficiency. First, we provide Bayesian network-based algorithms for losslessly compressing large discrete datasets. We show that these algorithms can produce compression ratios dramatically higher than those achieved by popular compression programs such as gzip or bzip2, yet still maintain megabyte-per-second decoding speeds on well-aged conventional PCs. Next, we provide algorithms for quickly learning Bayesian network-based probability models over domains with both discrete and continuous variables. We show how recently developed methods for quickly learning Gaussian mixture models from data [Moo99] can be used to learn Bayesian networks modeling complex nonlinear relationships over dozens of variables from thousands of datapoints in a practical amount of time. Finally we explore a large space of tree-based density learning algorithms, and show that they can be used to quickly learn Bayesian networks that can provide accurate density estimates and that are fast to evaluate."
Author : A. Holst
Publisher : IOS Press
ISBN 13 : 1607503352
Total Pages : 228 pages
Book Rating : 4.6/5 (75 download)
Download or read book Tenth Scandinavian Conference on Artificial Intelligence written by A. Holst and published by IOS Press. This book was released on 2008-05-19 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Scandinavian Conference on Artificial Intelligence continues a tradition of being one of the most important regional AI conferences in Europe for ten years now. The topics of this year’s contributions have a broad range, from machine learning, knowledge representation, robotics, planning and scheduling, natural language, computer vision, search algorithms, industrial applications, to philosophical foundations. These contributions exemplify the diversity of research in artificial intelligence today and confirm the achievement and magnitude of 25 years AI research in Scandinavia. In this tenth edition there will be an overview of the past, present and future of artificial intelligence. Furthermore, attention will be paid to the industrial aspects of artificial intelligence and the impressions from Swedish AI through the years. Other topics discussed are biosurveillance and an elaboration on probalistic modelling and learning in a relational world.
Author : Mirosław Kutyłowski
Publisher : Springer Nature
ISBN 13 : 3030657450
Total Pages : 458 pages
Book Rating : 4.0/5 (36 download)
Download or read book Network and System Security written by Mirosław Kutyłowski and published by Springer Nature. This book was released on 2020-12-18 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th International Conference on Network and System Security, NSS 2020, held in Melbourne, VIC, Australia, in November 2020. The 17 full and 9 short papers were carefully reviewed and selected from 60 submissions. The selected papers are devoted to topics such as secure operating system architectures, applications programming and security testing, intrusion and attack detection, cybersecurity intelligence, access control, cryptographic techniques, cryptocurrencies, ransomware, anonymity, trust, recommendation systems, as well machine learning problems. Due to the Corona pandemic the event was held virtually.
Author : Ilias Maglogiannis
Publisher : Springer Nature
ISBN 13 : 3031632230
Total Pages : 406 pages
Book Rating : 4.0/5 (316 download)
Download or read book Artificial Intelligence Applications and Innovations written by Ilias Maglogiannis and published by Springer Nature. This book was released on with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Yuan Xu
Publisher : Springer Nature
ISBN 13 : 3031200969
Total Pages : 694 pages
Book Rating : 4.0/5 (312 download)
Download or read book Machine Learning for Cyber Security written by Yuan Xu and published by Springer Nature. This book was released on 2023-01-12 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume proceedings set LNCS 13655,13656 and 13657 constitutes the refereedproceedings of the 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022, which taking place during December 2–4, 2022, held in Guangzhou, China. The 100 full papers and 46 short papers were included in these proceedings were carefully reviewed and selected from 367 submissions.
Author : Nizar Bouguila
Publisher : Springer
ISBN 13 : 3030238768
Total Pages : 355 pages
Book Rating : 4.0/5 (32 download)
Download or read book Mixture Models and Applications written by Nizar Bouguila and published by Springer. This book was released on 2019-08-13 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.
Author : Oswald Campesato
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 1501518658
Total Pages : 271 pages
Book Rating : 4.5/5 (15 download)
Download or read book Data Literacy with Python written by Oswald Campesato and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-11-20 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to usher readers into the world of data, ensuring a comprehensive understanding of its nuances, intricacies, and complexities. With Python 3 as the primary medium, the book underscores the pivotal role of data in modernindustries, and how its adept management can lead to insightful decision-making. The book provides a quick introduction to foundational data-related tasks, priming the readers for more advanced concepts of model training introduced later on. Through detailed, step-by-step Python code examples, the reader will master training models, beginning with the kNN algorithm, and then smoothly transitioning to other classifiers, by tweaking mere lines of code. Tools like Sweetviz, Skimpy, Matplotlib, and Seaborn are introduced, offering readers a hands-on experience in rendering charts and graphs. Companion files with source code and data sets are available by writing to the publisher. FEATURES: Introduces tools like Sweetviz, Skimpy, Matplotlib, and Seaborn offering readers a hands-on experience in rendering charts and graphs Companion files with numerous Python code samples
Author : Shuai Fu
Publisher :
ISBN 13 :
Total Pages : 45 pages
Book Rating : 4.:/5 (113 download)
Download or read book Bayesian Learning of Asymmetric Gaussian-Based Statistical Models Using Markov Chain Monte Carlo Techniques written by Shuai Fu and published by . This book was released on 2018 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: A novel unsupervised Bayesian learning framework based on asymmetric Gaussian mixture (AGM) statistical model is proposed since AGM is shown to be more effective compared to the classic Gaussian mixture. The Bayesian learning framework is developed by adopting sampling-based Markov chain Monte Carlo (MCMC) methodology. More precisely, the fundamental learning algorithm is a hybrid Metropolis-Hastings within Gibbs sampling solution which is integrated within a reversible jump MCMC (RJMCMC) learning framework, a self-adapted sampling-based MCMC implementation, that enables model transfer throughout the mixture parameters learning process, therefore, automatically converges to the optimal number of data groups. Furthermore, a feature selection technique is included to tackle the irrelevant and unneeded information from datasets. The performance comparison between AGM and other popular solutions is given and both synthetic and real data sets extracted from challenging applications such as intrusion detection, spam filtering and image categorization are evaluated to show the merits of the proposed approach.
Author : Payal Khurana Batra
Publisher : CRC Press
ISBN 13 : 1040118771
Total Pages : 349 pages
Book Rating : 4.0/5 (41 download)
Download or read book Network Optimization in Intelligent Internet of Things Applications written by Payal Khurana Batra and published by CRC Press. This book was released on 2024-09-25 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Network Optimization in Intelligent Internet of Things Applications: Principles and Challenges sheds light on the optimization methods that form the basis of effective communication between networked devices. It is an excellent resource as it provides readers with a thorough understanding of the methods, ideas, and tactics essential to attaining seamless connectivity and improving performance. This book presents the fundamental ideas that govern network optimization, from maximizing throughput and lowering latency to handling a variety of communication protocols and minimizing energy use. It also addresses scalability issues, security flaws, and constantly changing IoT environments along with optimization techniques. This book uses cutting-edge research and real-world examples to give readers the knowledge and skills to address the complex problems associated with network optimization in intelligent IoT applications. It also examines machine learning-driven predictive analytics, robust security protocols, flexible routing algorithms, and the integration of edge computing - all crucial instruments for overcoming obstacles and attaining peak performance. This book provides a comprehensive understanding of the principles, challenges, and cutting-edge solutions in IoT network optimization for all kinds of readers, whether it is students, academicians, researchers, or industry professionals. This book unleashes the potential of networked smart devices, which can be unleashed in various sectors.
Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1789349273
Total Pages : 375 pages
Book Rating : 4.7/5 (893 download)
Download or read book Hands-On Unsupervised Learning with Python written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2019-02-28 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the skill-sets required to implement various approaches to Machine Learning with Python Key FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learnUse cluster algorithms to identify and optimize natural groups of dataExplore advanced non-linear and hierarchical clustering in actionSoft label assignments for fuzzy c-means and Gaussian mixture modelsDetect anomalies through density estimationPerform principal component analysis using neural network modelsCreate unsupervised models using GANsWho this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
Author : Aurélien Géron
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 149203259X
Total Pages : 851 pages
Book Rating : 4.4/5 (92 download)
Download or read book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Author : Ziyang Song
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)
Download or read book Nonparametric Bayesian Models Based on Asymmetric Gaussian Distributions written by Ziyang Song and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data clustering is a fundamental unsupervised learning approach that impacts several domains such as data mining, computer vision, information retrieval, and pattern recognition. Various clustering techniques have been introduced over the years to discover the patterns. Mixture model is one of the most promising techniques for clustering. The design of mixture models hence involves finding the appropriate parameters and estimating the number of clusters in the data. The Gaussian mixture model has especially shown good results to tackle this problem. However, the Gaussian assumption is not ideal for modeling asymmetrical data. For achieving an accurate approximation, I investigate the asymmetric Gaussian distribution which is capable of modeling asymmetric data. A prevalent challenge researchers face when applying mixture models is the correct identification of the adequate number of mixture components to model the data at hand. Hence, in this thesis, I propose statistical algorithms based on asymmetric Gaussian mixture models. I also present novel Bayesian inference frameworks to estimate parameters and learn model structure. Here, I thoroughly investigate the Bayesian inference framework, including Markov chain Monte Carlo and variational inference approaches, to learn appropriate model structure and precisely estimate parameters. I also incorporate feature selection within the frameworks to choose relevant features set and avoid noisy influence from uninformative features. Furthermore, I investigate nonparametric hierarchical models by introducing Dirichlet process and Pitman-Yor process.
Author : Stan Z. Li
Publisher : Springer Science & Business Media
ISBN 13 : 0387730028
Total Pages : 1466 pages
Book Rating : 4.3/5 (877 download)
Download or read book Encyclopedia of Biometrics written by Stan Z. Li and published by Springer Science & Business Media. This book was released on 2009-08-27 with total page 1466 pages. Available in PDF, EPUB and Kindle. Book excerpt: With an A–Z format, this encyclopedia provides easy access to relevant information on all aspects of biometrics. It features approximately 250 overview entries and 800 definitional entries. Each entry includes a definition, key words, list of synonyms, list of related entries, illustration(s), applications, and a bibliography. Most entries include useful literature references providing the reader with a portal to more detailed information.
Author : Henry Selvaraj
Publisher : Springer
ISBN 13 : 3319084224
Total Pages : 846 pages
Book Rating : 4.3/5 (19 download)
Download or read book Progress in Systems Engineering written by Henry Selvaraj and published by Springer. This book was released on 2014-08-12 with total page 846 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of proceedings from the International Conference on Systems Engineering, Las Vegas, 2014 is orientated toward systems engineering, including topics like aero-space, power systems, industrial automation and robotics, systems theory, control theory, artificial intelligence, signal processing, decision support, pattern recognition and machine learning, information and communication technologies, image processing, and computer vision as well as its applications. The volume’s main focus is on models, algorithms, and software tools that facilitate efficient and convenient utilization of modern achievements in systems engineering.
