Network Anomaly Detection

Author : Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publisher : CRC Press
Page : 366 pages
File Size : 41,6 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 : Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita
Publisher : Springer
Page : 263 pages
File Size : 43,8 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 : Jugal Kalita
Publisher : Unknown
Page : 366 pages
File Size : 43,9 Mb
Release : 2013
Category : Electronic
ISBN : OCLC:1137345227

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Network Anomaly Detection by Jugal 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 : Jordi Domingo-Pascual,Pietro Manzoni,Sergio Palazzo,Ana Pont,Caterina Scoglio
Publisher : Springer Science & Business Media
Page : 492 pages
File Size : 47,5 Mb
Release : 2011-04-28
Category : Business & Economics
ISBN : 9783642207563

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NETWORKING 2011 by Jordi Domingo-Pascual,Pietro Manzoni,Sergio Palazzo,Ana Pont,Caterina Scoglio Pdf

The two-volume set LNCS 6640 and 6641 constitutes the refereed proceedings of the 10th International IFIP TC 6 Networking Conference held in Valencia, Spain, in May 2011. The 64 revised full papers presented were carefully reviewed and selected from a total of 294 submissions. The papers feature innovative research in the areas of applications and services, next generation Internet, wireless and sensor networks, and network science. The first volume includes 36 papers and is organized in topical sections on anomaly detection, content management, DTN and sensor networks, energy efficiency, mobility modeling, network science, network topology configuration, next generation Internet, and path diversity.

Author : Rachid Benlamri
Publisher : Springer
Page : 0 pages
File Size : 49,6 Mb
Release : 2012-06-16
Category : Computers
ISBN : 3642305067

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Networked Digital Technologies by Rachid Benlamri Pdf

This two-volume-set (CCIS 293 and CCIS 294) constitutes the refereed proceedings of the International Conference on Networked Digital Technologies, NDT 2012, held in Dubai, UAE, in April 2012. The 96 papers presented in the two volumes were carefully reviewed and selected from 228 submissions. The papers are organized in topical sections on collaborative systems for e-sciences; context-aware processing and ubiquitous systems; data and network mining; grid and cloud computing; information and data management; intelligent agent-based systems; internet modeling and design; mobile, ad hoc and sensor network management; peer-to-peer social networks; quality of service for networked systems; semantic Web and ontologies; security and access control; signal processing and computer vision for networked systems; social networks; Web services.

Author : Zahir Tari,Adil Fahad,Xun Yi,Abdulmohsen Almalawi
Publisher : Computing and Networks
Page : 291 pages
File Size : 49,6 Mb
Release : 2020-03-23
Category : Computers
ISBN : 9781785619212

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Network Classification for Traffic Management by Zahir Tari,Adil Fahad,Xun Yi,Abdulmohsen Almalawi Pdf

This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks.

Author : Anonim
Publisher : BoD – Books on Demand
Page : 170 pages
File Size : 55,8 Mb
Release : 2024-01-17
Category : Electronic
ISBN : 9781837690268

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Anomaly Detection by Anonim Pdf

Author : Sridhar Alla,Suman Kalyan Adari
Publisher : Apress
Page : 427 pages
File Size : 40,5 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 : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 47,8 Mb
Release : 2015
Category : Electronic
ISBN : OCLC:932219992

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Entropy-based Network Anomaly Detection by Anonim Pdf

Author : Danfeng (Daphne)Yao,Xiaokui Shu,Long Cheng,Salvatore J.Stolfo
Publisher : Springer Nature
Page : 157 pages
File Size : 50,6 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 : Matthew Moocarme,Anthony So,Anthony Maddalone
Publisher : Packt Publishing Ltd
Page : 601 pages
File Size : 40,5 Mb
Release : 2021-12-15
Category : Computers
ISBN : 9781800200227

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The TensorFlow Workshop by Matthew Moocarme,Anthony So,Anthony Maddalone Pdf

Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activities Key FeaturesUnderstand the fundamentals of tensors, neural networks, and deep learningDiscover how to implement and fine-tune deep learning models for real-world datasetsBuild your experience and confidence with hands-on exercises and activitiesBook Description Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it'll quickly get you up and running. You'll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you'll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you'll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you'll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow. What you will learnGet to grips with TensorFlow's mathematical operationsPre-process a wide variety of tabular, sequential, and image dataUnderstand the purpose and usage of different deep learning layersPerform hyperparameter-tuning to prevent overfitting of training dataUse pre-trained models to speed up the development of learning modelsGenerate new data based on existing patterns using generative modelsWho this book is for This TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.

Author : Ouyang, Ye,Hu, Mantian
Publisher : IGI Global
Page : 216 pages
File Size : 45,6 Mb
Release : 2016-12-28
Category : Computers
ISBN : 9781522517511

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Big Data Applications in the Telecommunications Industry by Ouyang, Ye,Hu, Mantian Pdf

The growing presence of smart phones and smart devices has caused significant changes to wireless networks. With the ubiquity of these technologies, there is now increasingly more available data for mobile operators to utilize. Big Data Applications in the Telecommunications Industry is a comprehensive reference source for the latest scholarly material on the use of data analytics to study wireless networks and examines how these techniques can increase reliability and profitability, as well as network performance and connectivity. Featuring extensive coverage on relevant topics, such as accessibility, traffic data, and customer satisfaction, this publication is ideally designed for engineers, students, professionals, academics, and researchers seeking innovative perspectives on data science and wireless network communications.

Author : Shi Jin,Zhaobo Zhang,Krishnendu Chakrabarty,Xinli Gu
Publisher : Springer Nature
Page : 155 pages
File Size : 49,5 Mb
Release : 2019-12-19
Category : Technology & Engineering
ISBN : 9783030336646

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Anomaly-Detection and Health-Analysis Techniques for Core Router Systems by Shi Jin,Zhaobo Zhang,Krishnendu Chakrabarty,Xinli Gu Pdf

This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status. Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis; Presents the design of a changepoint-based anomaly detector; Includes Hierarchical Symbol-based Health-Status Analysis; Describes an iterative, self-learning procedure for assessing the health status.

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 : 52,9 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.

Author : Niall Adams,Nick Heard
Publisher : World Scientific
Page : 224 pages
File Size : 44,5 Mb
Release : 2016-03-22
Category : Electronic
ISBN : 9781786340764

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Dynamic Networks and Cyber-Security by Niall Adams,Nick Heard Pdf

As an under-studied area of academic research, the analysis of computer network traffic data is still in its infancy. However, the challenge of detecting and mitigating malicious or unauthorised behaviour through the lens of such data is becoming an increasingly prominent issue. This collection of papers by leading researchers and practitioners synthesises cutting-edge work in the analysis of dynamic networks and statistical aspects of cyber security. The book is structured in such a way as to keep security application at the forefront of discussions. It offers readers easy access into the area of data analysis for complex cyber-security applications, with a particular focus on temporal and network aspects. Chapters can be read as standalone sections and provide rich reviews of the latest research within the field of cyber-security. Academic readers will benefit from state-of-the-art descriptions of new methodologies and their extension to real practical problems while industry professionals will appreciate access to more advanced methodology than ever before. Contents:Network Attacks and the Data They Affect (M Morgan, J Sexton, J Neil, A Ricciardi & J Theimer)Cyber-Security Data Sources for Dynamic Network Research (A D Kent)Modelling User Behaviour in a Network Using Computer Event Logs (M J M Turcotte, N A Heard & A D Kent)Network Services as Risk Factors: A Genetic Epidemiology Approach to Cyber-Security (S Gil)Community Detection and Role Identification in Directed Networks: Understanding the Twitter Network of the Care.Data Debate (B Amor, S Vuik, R Callahan, A Darzi, S N Yaliraki & M Barahona)Anomaly Detection for Cyber Security Applications (P Rubin-Delanchy, D J Lawson & N A Heard)Exponential Random Graph Modelling of Static and Dynamic Social Networks (A Caimo)Hierarchical Dynamic Walks (A V Mantzaris, P Grindrod & D J Higham)Temporal Reachability in Dynamic Networks (A Hagberg, N Lemons & S Misra) Readership: Researchers and practitioners in dynamic network analysis and cyber-security. Key Features:Detailed descriptions of the behaviour of attackersDiscussions of new public domain data sources, including data quality issuesA collection of papers introducing novel methodology for cyber-data analysis