Anomaly Detection As A Service

Author : Danfeng (Daphne)Yao,Xiaokui Shu,Long Cheng,Salvatore J.Stolfo
Publisher : Springer Nature
Page : 157 pages
File Size : 43,5 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031023545

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Anomaly Detection as a Service by Danfeng (Daphne)Yao,Xiaokui Shu,Long Cheng,Salvatore J.Stolfo Pdf

Anomaly detection has been a long-standing security approach with versatile applications, ranging from securing server programs in critical environments, to detecting insider threats in enterprises, to anti-abuse detection for online social networks. Despite the seemingly diverse application domains, anomaly detection solutions share similar technical challenges, such as how to accurately recognize various normal patterns, how to reduce false alarms, how to adapt to concept drifts, and how to minimize performance impact. They also share similar detection approaches and evaluation methods, such as feature extraction, dimension reduction, and experimental evaluation. The main purpose of this book is to help advance the real-world adoption and deployment anomaly detection technologies, by systematizing the body of existing knowledge on anomaly detection. This book is focused on data-driven anomaly detection for software, systems, and networks against advanced exploits and attacks, but also touches on a number of applications, including fraud detection and insider threats. We explain the key technical components in anomaly detection workflows, give in-depth description of the state-of-the-art data-driven anomaly-based security solutions, and more importantly, point out promising new research directions. This book emphasizes on the need and challenges for deploying service-oriented anomaly detection in practice, where clients can outsource the detection to dedicated security providers and enjoy the protection without tending to the intricate details.

Author : Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita
Publisher : Springer
Page : 263 pages
File Size : 46,6 Mb
Release : 2017-09-03
Category : Computers
ISBN : 9783319651880

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Network Traffic Anomaly Detection and Prevention by Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita Pdf

This indispensable text/reference presents a comprehensive overview on the detection and prevention of anomalies in computer network traffic, from coverage of the fundamental theoretical concepts to in-depth analysis of systems and methods. Readers will benefit from invaluable practical guidance on how to design an intrusion detection technique and incorporate it into a system, as well as on how to analyze and correlate alerts without prior information. Topics and features: introduces the essentials of traffic management in high speed networks, detailing types of anomalies, network vulnerabilities, and a taxonomy of network attacks; describes a systematic approach to generating large network intrusion datasets, and reviews existing synthetic, benchmark, and real-life datasets; provides a detailed study of network anomaly detection techniques and systems under six different categories: statistical, classification, knowledge-base, cluster and outlier detection, soft computing, and combination learners; examines alert management and anomaly prevention techniques, including alert preprocessing, alert correlation, and alert post-processing; presents a hands-on approach to developing network traffic monitoring and analysis tools, together with a survey of existing tools; discusses various evaluation criteria and metrics, covering issues of accuracy, performance, completeness, timeliness, reliability, and quality; reviews open issues and challenges in network traffic anomaly detection and prevention. This informative work is ideal for graduate and advanced undergraduate students interested in network security and privacy, intrusion detection systems, and data mining in security. Researchers and practitioners specializing in network security will also find the book to be a useful reference.

Author : Saira Banu
Publisher : Nova Science Publishers
Page : 0 pages
File Size : 49,6 Mb
Release : 2021
Category : Anomaly detection (Computer security)
ISBN : 1536192643

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Anomaly Detection by Saira Banu Pdf

When information in the data warehouse is processed, it follows a definite pattern. An unexpected deviation in the data pattern from the usual behavior is called an anomaly. The anomaly in the data is also referred to as noise, outlier, spammer, deviations, novelties and exceptions. Identification of the rare items, events, observations, patterns which raise suspension by differing significantly from the majority of data is called anomaly detection. With progress in the technologies and the widespread use of data for the purpose for business the increase in the spams faced by the individuals and the companies are increasing day by day. This noisy data has boomed as a major problem in various areas such as Internet of Things, web service, Machine Learning, Artificial Intelligence, Deep learning, Image Processing, Cloud Computing, Audio processing, Video Processing, VoIP, Data Science, Wireless Sensor etc. Identifying the anomaly data and filtering them before processing is a major challenge for the data analyst. This anomaly is unavoidable in all areas of research. This book covers the techniques and algorithms for detecting the deviated data. This book will mainly target researchers and higher graduate learners in computer science and data science.

Author : Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publisher : CRC Press
Page : 366 pages
File Size : 43,5 Mb
Release : 2013-06-18
Category : Computers
ISBN : 9781466582095

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Network Anomaly Detection by Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita Pdf

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 : Anonim
Publisher : BoD – Books on Demand
Page : 170 pages
File Size : 54,9 Mb
Release : 2024-01-17
Category : Electronic
ISBN : 9781837690268

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Anomaly Detection by Anonim Pdf

Author : Kevin Feasel
Publisher : Apress
Page : 0 pages
File Size : 43,7 Mb
Release : 2022-11-22
Category : Computers
ISBN : 1484288696

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Finding Ghosts in Your Data by Kevin Feasel Pdf

Discover key information buried in the noise of data by learning a variety of anomaly detection techniques and using the Python programming language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and outliers are and uses the Gestalt school of psychology to explain just why it is that humans are naturally great at detecting anomalies. From there, you will move into technical definitions of anomalies, moving beyond "I know it when I see it" to defining things in a way that computers can understand. The core of the book involves building a robust, deployable anomaly detection service in Python. You will start with a simple anomaly detection service, which will expand over the course of the book to include a variety of valuable anomaly detection techniques, covering descriptive statistics, clustering, and time series scenarios. Finally, you will compare your anomaly detection service head-to-head with a publicly available cloud offering and see how they perform. The anomaly detection techniques and examples in this book combine psychology, statistics, mathematics, and Python programming in a way that is easily accessible to software developers. They give you an understanding of what anomalies are and why you are naturally a gifted anomaly detector. Then, they help you to translate your human techniques into algorithms that can be used to program computers to automate the process. You’ll develop your own anomaly detection service, extend it using a variety of techniques such as including clustering techniques for multivariate analysis and time series techniques for observing data over time, and compare your service head-on against a commercial service. What You Will Learn Understand the intuition behind anomalies Convert your intuition into technical descriptions of anomalous data Detect anomalies using statistical tools, such as distributions, variance and standard deviation, robust statistics, and interquartile range Apply state-of-the-art anomaly detection techniques in the realms of clustering and time series analysis Work with common Python packages for outlier detection and time series analysis, such as scikit-learn, PyOD, and tslearn Develop a project from the ground up which finds anomalies in data, starting with simple arrays of numeric data and expanding to include multivariate inputs and even time series data Who This Book Is For For software developers with at least some familiarity with the Python programming language, and who would like to understand the science and some of the statistics behind anomaly detection techniques. Readers are not required to have any formal knowledge of statistics as the book introduces relevant concepts along the way.

Author : Charu C. Aggarwal,Saket Sathe
Publisher : Springer
Page : 276 pages
File Size : 41,9 Mb
Release : 2017-04-06
Category : Computers
ISBN : 9783319547657

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Outlier Ensembles by Charu C. Aggarwal,Saket Sathe Pdf

This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.

Author : Rolf Stadler,Burkhard Stiller
Publisher : Springer
Page : 312 pages
File Size : 43,8 Mb
Release : 2003-07-31
Category : Computers
ISBN : 9783540481003

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Active Technologies for Network and Service Management by Rolf Stadler,Burkhard Stiller Pdf

This volume of the Lecture Notes in Computer Science series contains all papers accepted for presentation at the 10th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management (DSOM’99), which took place at the ETH Zürich in Switzerland and was hosted by the Computer Engineering and Networking Laboratory, TIK. DSOM’99 is the tenth workshop in a series of annual workshops, and Zürich is proud to host this 10th anniversary of the IEEE/IFIP workshop. DSOM’99 follows highly successful meetings, the most recent of which took place in Delaware, U.S.A. (DSOM'98), Sydney, Australia (DSOM'97), and L’Aquila, Italy (DSOM'96). DSOM workshops attempt to bring together researchers from the area of network and service management in both industry and academia to discuss recent advancements and to foster further growth in this ?eld. In contrast to the larger management symposia IM (In- grated Network Management) and NOMS (Network Operations and Management S- posium), DSOM workshops follow a single-track program, in order to stimulate interaction and active participation. The speci?c focus of DSOM’99 is “Active Technologies for Network and Service Management,” re?ecting the current developments in the ?eld of active and program- ble networks, and about half of the papers in this workshop fall within this category.

Author : Sridhar Alla,Suman Kalyan Adari
Publisher : Apress
Page : 427 pages
File Size : 44,5 Mb
Release : 2019-10-10
Category : Computers
ISBN : 9781484251775

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Beginning Anomaly Detection Using Python-Based Deep Learning by Sridhar Alla,Suman Kalyan Adari Pdf

Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You Will LearnUnderstand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection Who This Book Is For Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection

Author : Mehdi Roopaei,Peyman Najafirad (Paul Rad)
Publisher : CRC Press
Page : 188 pages
File Size : 43,5 Mb
Release : 2018-04-09
Category : Computers
ISBN : 9781351119016

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Applied Cloud Deep Semantic Recognition by Mehdi Roopaei,Peyman Najafirad (Paul Rad) Pdf

This book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issue in real application domains. This book provides a better understanding of the different directions in which research has been done on deep semantic analysis and situational assessment using deep learning for anomalous detection, and how methods developed in one area can be applied in applications in other domains. This book seeks to provide both cyber analytics practitioners and researchers an up-to-date and advanced knowledge in cloud based frameworks for deep semantic analysis and advanced anomaly detection using cognitive and artificial intelligence (AI) models.

Author : Patrick Schneider,Fatos Xhafa
Publisher : Academic Press
Page : 408 pages
File Size : 41,7 Mb
Release : 2022-01-07
Category : Computers
ISBN : 9780128238196

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Anomaly Detection and Complex Event Processing Over IoT Data Streams by Patrick Schneider,Fatos Xhafa Pdf

Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing. Provides the state-of-the-art in IoT Data Stream Processing, Semantic Data Enrichment, Reasoning and Knowledge Covers extraction (Anomaly Detection) Illustrates new, scalable and reliable processing techniques based on IoT stream technologies Offers applications to new, real-time anomaly detection scenarios in the health domain

Author : Vincent G. Duffy
Publisher : Springer
Page : 631 pages
File Size : 52,6 Mb
Release : 2016-07-04
Category : Computers
ISBN : 9783319402475

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Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management by Vincent G. Duffy Pdf

This book constitutes the refereed proceedings of the 7th International Conference on Digital Human Modelling: Applications in Health, Safety, Ergonomics and Risk Management, DHM 2016, held as part of the 18th International Conference on Human-Computer Interaction, HCII 2016, held in Toronto, ON, Canada, in July 2016 and received a total of 4354 submissions, of which 1287 papers were accepted for publication after a careful reviewing process. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. This volume contains papers addressing the following major topics: anthropometry, ergonomics, design and comfort; physiology and anatomy models; motion prediction and recognition; quality and safety in healthcare; design for health; work design and support; modeling human behavior and cognition.

Author : Jeng-Shyang Pan,Shyi-Ming Chen,Ngoc Thanh Nguyen
Publisher : Springer Science & Business Media
Page : 546 pages
File Size : 49,8 Mb
Release : 2012-03-02
Category : Computers
ISBN : 9783642284922

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Intelligent Information and Database Systems by Jeng-Shyang Pan,Shyi-Ming Chen,Ngoc Thanh Nguyen Pdf

The three-volume set LNAI 7196, LNAI 7197 and LNAI 7198 constitutes the refereed proceedings of the 4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012, held in Kaohsiung, Taiwan in March 2012. The 161 revised papers presented were carefully reviewed and selected from more than 472 submissions. The papers included cover the following topics: intelligent database systems, data warehouses and data mining, natural language processing and computational linguistics, semantic Web, social networks and recommendation systems, collaborative systems and applications, e-bussiness and e-commerce systems, e-learning systems, information modeling and requirements engineering, information retrieval systems, intelligent agents and multi-agent systems, intelligent information systems, intelligent internet systems, intelligent optimization techniques, object-relational DBMS, ontologies and knowledge sharing, semi-structured and XML database systems, unified modeling language and unified processes, Web services and semantic Web, computer networks and communication systems.

Author : Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publisher : CRC Press
Page : 368 pages
File Size : 44,6 Mb
Release : 2013-06-18
Category : Computers
ISBN : 9781466582088

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Network Anomaly Detection by Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita Pdf

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 : Eduardo H.M. Pena ,Luiz F. Carvalho ,Sylvio Barbon ,Joel J.P.C. Rodrigues ,Mario Lemes Proença
Publisher : Infinite Study
Page : 16 pages
File Size : 51,9 Mb
Release : 2024-05-11
Category : Electronic
ISBN : 8210379456XXX

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Anomaly detection using the correlational paraconsistent machine with digital signatures of network segment by Eduardo H.M. Pena ,Luiz F. Carvalho ,Sylvio Barbon ,Joel J.P.C. Rodrigues ,Mario Lemes Proença Pdf

This study presents the correlational paraconsistent machine (CPM), a tool for anomaly detection that incorporates unsupervised models for traffic characterization and principles of paraconsistency, to inspect irregularities at the network traffic flow level.