Network Anomaly Detection
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Network Anomaly Detection
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Author : Dhruba Kumar Bhattacharyya
language : en
Publisher: CRC Press
Release Date : 2013-06-18
Network Anomaly Detection written by Dhruba Kumar Bhattacharyya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-18 with Computers categories.
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.
Network Traffic Anomaly Detection And Prevention
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Author : Monowar H. Bhuyan
language : en
Publisher: Springer
Release Date : 2017-09-03
Network Traffic Anomaly Detection And Prevention written by Monowar H. Bhuyan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-03 with Computers categories.
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.
Network Anomaly Detection
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Author : Jugal Kalita
language : en
Publisher:
Release Date : 2013
Network Anomaly Detection written by Jugal Kalita 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.
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.
Network Anomaly Detection
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Author : Dhruba Kumar Bhattacharyya
language : en
Publisher: CRC Press
Release Date : 2013-06-18
Network Anomaly Detection written by Dhruba Kumar Bhattacharyya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-18 with Computers categories.
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 behavi
Outlier Detection Techniques And Applications
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Author : N. N. R. Ranga Suri
language : en
Publisher: Springer
Release Date : 2019-01-10
Outlier Detection Techniques And Applications written by N. N. R. Ranga Suri and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-10 with Computers categories.
This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.
Outlier Ensembles
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2017-04-06
Outlier Ensembles written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-06 with Computers categories.
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.
Network Anomaly Detection Based On Late Fusion Of Several Machine Learning Algorithms
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Author :
language : en
Publisher:
Release Date : 2021
Network Anomaly Detection Based On Late Fusion Of Several Machine Learning Algorithms written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Today's Internet and enterprise networks are so popular as they can easily provide multimedia and ecommerce services to millions of users over the Internet in our daily lives. Since then, security has been a challenging problem in the Internet's world. That issue is called Cyberwar, in which attackers can aim or raise Distributed Denial of Service (DDoS) to others to take down the operation of enterprises Intranet. Therefore, the need of applying an Intrusion Detection System (IDS) is very important to enterprise networks. In this paper, we propose a smarter solution to detect network anomalies in Cyberwar using Stacking techniques in which we apply three popular machine learning models: k-nearest neighbor algorithm (KNN), Adaptive Boosting (AdaBoost), and Random Decision Forests (RandomForest). Our proposed scheme uses the Logistic Regression method to automatically search for better parameters to the Stacking model. We do the performance evaluation of our proposed scheme on the latest data set NSLKDD 2019 dataset. We also compare the achieved results with individual machine learning models to show that our proposed model achieves much higher accuracy than previous works.
2019 Ieee 5th International Conference For Convergence In Technology I2ct
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Author : IEEE Staff
language : en
Publisher:
Release Date : 2019-03-29
2019 Ieee 5th International Conference For Convergence In Technology I2ct written by IEEE Staff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-29 with categories.
The scope of conference papers and exhibits including but not limited to the following area related to ELECTRONICS AND COMMUNICATION ENGG, ELECTRICAL ENGINEERING , INFORMATION TECHNOLOGY COMPUTER ENGINEERING WIRELESS NETWORKING COMPUTATIONAL INTELLIGENCE ADVANCED COMPUTING ELECTRONICS AND INTERDISCIPLINARY DATA COMMUNICATION AND NETWORKING RENEWABLE AND SUSTAINABLE ENERGY POWER ENGINEERING AND CONTROL SYSTEM SIGNAL AND IMAGE PROCESSING COMMUNICATION SYSTEM BIOMEDICAL ENGINEERING DESIGN, MATERIALS AND MANUFACTURING FLEET TECHNOLOGIES ADVANCES IN CIVIL AND ENVIRONMENTAL ENGINEERING SPECIAL CALL FOR PAPERS CONVERGENCE IN TECHNOLOGY
An Analysis Of Anomaly Detection In Network Traffic And Role Of Wavelets
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Author : Gagandeep Kaur
language : en
Publisher:
Release Date : 2010
An Analysis Of Anomaly Detection In Network Traffic And Role Of Wavelets written by Gagandeep Kaur and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Detection of anomalies in today's world is a cumbersome task due to highly dynamic nature of intrusions. The traditional practices of Network Anomaly Detection (NAD) fail to detect and identify the dynamic intrusions in real times. They are based on patterns initially saved in database. Large number of tools exists in open source as well as commercial market, but fast and accurate detection and identification of anomalies still remains a gruesome task. In the past few years signal processing techniques have found applications in Network Intrusion Detection (NID) Systems due to their efficiency in tracing out deviations as well as transformations in the network traffic data. Any Intrusion Detection System (IDS) needs excellent visualization of obtained results so as to provide network administrator good information in the least possible time. This survey explains the main techniques known in the field of statistical-based and wavelet-based anomaly detection approaches and focuses on the role of data traffic visualization tools in network traffic anomaly detection.