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Network Intrusion Analysis

Network Intrusion Analysis
Methodologies, Tools, and Techniques for Incident Analysis and Response

by Joe Fichera,Steven Bolt

  • Publisher : Newnes
  • Release : 2012-12-17
  • Pages : 252
  • ISBN : 1597499714
  • Language : En, Es, Fr & De
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Nearly every business depends on its network to provide information services to carry out essential activities, and network intrusion attacks have been growing increasingly frequent and severe. When network intrusions do occur, it’s imperative that a thorough and systematic analysis and investigation of the attack is conducted to determine the nature of the threat and the extent of information lost, stolen, or damaged during the attack. A thorough and timely investigation and response can serve to minimize network downtime and ensure that critical business systems are maintained in full operation. Network Intrusion Analysis teaches the reader about the various tools and techniques to use during a network intrusion investigation. The book focuses on the methodology of an attack as well as the investigative methodology, challenges, and concerns. This is the first book that provides such a thorough analysis of network intrusion investigation and response. Network Intrusion Analysis addresses the entire process of investigating a network intrusion by: *Providing a step-by-step guide to the tools and techniques used in the analysis and investigation of a network intrusion. *Providing real-world examples of network intrusions, along with associated workarounds. *Walking you through the methodology and practical steps needed to conduct a thorough intrusion investigation and incident response, including a wealth of practical, hands-on tools for incident assessment and mitigation. Network Intrusion Analysis addresses the entire process of investigating a network intrusion Provides a step-by-step guide to the tools and techniques used in the analysis and investigation of a network intrusion Provides real-world examples of network intrusions, along with associated workarounds Walks readers through the methodology and practical steps needed to conduct a thorough intrusion investigation and incident response, including a wealth of practical, hands-on tools for incident assessment and mitigation

Practical Intrusion Analysis

Practical Intrusion Analysis
Prevention and Detection for the Twenty-First Century

by Ryan Trost

  • Publisher : Pearson Education
  • Release : 2009-06-24
  • Pages : 480
  • ISBN : 9780321591883
  • Language : En, Es, Fr & De
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“Practical Intrusion Analysis provides a solid fundamental overview of the art and science of intrusion analysis.” –Nate Miller, Cofounder, Stratum Security The Only Definitive Guide to New State-of-the-Art Techniques in Intrusion Detection and Prevention Recently, powerful innovations in intrusion detection and prevention have evolved in response to emerging threats and changing business environments. However, security practitioners have found little reliable, usable information about these new IDS/IPS technologies. In Practical Intrusion Analysis, one of the field’s leading experts brings together these innovations for the first time and demonstrates how they can be used to analyze attacks, mitigate damage, and track attackers. Ryan Trost reviews the fundamental techniques and business drivers of intrusion detection and prevention by analyzing today’s new vulnerabilities and attack vectors. Next, he presents complete explanations of powerful new IDS/IPS methodologies based on Network Behavioral Analysis (NBA), data visualization, geospatial analysis, and more. Writing for security practitioners and managers at all experience levels, Trost introduces new solutions for virtually every environment. Coverage includes Assessing the strengths and limitations of mainstream monitoring tools and IDS technologies Using Attack Graphs to map paths of network vulnerability and becoming more proactive about preventing intrusions Analyzing network behavior to immediately detect polymorphic worms, zero-day exploits, and botnet DoS attacks Understanding the theory, advantages, and disadvantages of the latest Web Application Firewalls Implementing IDS/IPS systems that protect wireless data traffic Enhancing your intrusion detection efforts by converging with physical security defenses Identifying attackers’ “geographical fingerprints” and using that information to respond more effectively Visualizing data traffic to identify suspicious patterns more quickly Revisiting intrusion detection ROI in light of new threats, compliance risks, and technical alternatives Includes contributions from these leading network security experts: Jeff Forristal, a.k.a. Rain Forest Puppy, senior security professional and creator of libwhisker Seth Fogie, CEO, Airscanner USA; leading-edge mobile security researcher; coauthor of Security Warrior Dr. Sushil Jajodia, Director, Center for Secure Information Systems; founding Editor-in-Chief, Journal of Computer Security Dr. Steven Noel, Associate Director and Senior Research Scientist, Center for Secure Information Systems, George Mason University Alex Kirk, Member, Sourcefire Vulnerability Research Team

Network Intrusion Detection

Network Intrusion Detection
A Book

by Stephen Northcutt,Judy Novak

  • Publisher : Sams Publishing
  • Release : 2002
  • Pages : 490
  • ISBN : 9780735712652
  • Language : En, Es, Fr & De
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This book is a training aid and reference for intrusion detection analysts. While the authors refer to research and theory, they focus their attention on providing practical information. New to this edition is coverage of packet dissection, IP datagram fields, forensics, and snort filters.

Operational Network Intrusion Detection

Operational Network Intrusion Detection
Resource-analysis Tradeoffs

by Holger Dreger

  • Publisher : Unknown Publisher
  • Release : 2007
  • Pages : 162
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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The Development of a Multimodal Decision Support System for Network Intrusion Detection Analysis

The Development of a Multimodal Decision Support System for Network Intrusion Detection Analysis
A Book

by Tanya Tamika Capers

  • Publisher : Unknown Publisher
  • Release : 2004
  • Pages : 238
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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The increasing accessibility of information and volumes of on-line transactions are a reflection of the growing number and sophistication of computer security incidents on the Internet. While an intrusion detection system may be one component of a good security model, implementing intrusion detection systems on networks and hosts requires a broad understanding of computer security, and the massive amounts of textual data retrieved by the system. Given the sensitivity of the security posture, interpretation for rapid response in maintaining operational security is perhaps one of the more prevalent problems in security operations. The purpose of this study, in essence, is to increase situational awareness of the security posture through the development of a multimodal decision support system for network intrusion analysis. Modality fusion can extend the capabilities of computer systems to better match the natural communication means of human beings and assist them in their comprehension and exploration of large unfamiliar datasets or information spaces. In this way, modality fusion embodies intelligence amplification and cognitive augmentation, using computers to aid and enhance human intelligence by utilizing inherent human cognitive abilities, building upon the skills that humans already have, and augmenting the areas that are lacking in some way. This research evaluates conventional and multimodal intrusion analysis environments, the effectiveness, efficiency, and the preference of modality combinations in attack identification while gauging performance. By integrating multiple modalities to provide a full picture of the security posture, we can better communicate the information that is needed by the analyst, and can elicit an increase in their situational awareness, reduce time necessary for decision-making, and thus increase performance. Preliminary results show that auditory as well as haptics benefit in search and alerting tasks. Incorporating auditory and haptics reduced the performance time to less than half that of the traditional visual approach. -- Abstract.

Network Security Through Data Analysis

Network Security Through Data Analysis
From Data to Action

by Michael Collins

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2017-09-08
  • Pages : 428
  • ISBN : 1491962798
  • Language : En, Es, Fr & De
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Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In the updated second edition of this practical guide, security researcher Michael Collins shows InfoSec personnel the latest techniques and tools for collecting and analyzing network traffic datasets. You’ll understand how your network is used, and what actions are necessary to harden and defend the systems within it. In three sections, this book examines the process of collecting and organizing data, various tools for analysis, and several different analytic scenarios and techniques. New chapters focus on active monitoring and traffic manipulation, insider threat detection, data mining, regression and machine learning, and other topics. You’ll learn how to: Use sensors to collect network, service, host, and active domain data Work with the SiLK toolset, Python, and other tools and techniques for manipulating data you collect Detect unusual phenomena through exploratory data analysis (EDA), using visualization and mathematical techniques Analyze text data, traffic behavior, and communications mistakes Identify significant structures in your network with graph analysis Examine insider threat data and acquire threat intelligence Map your network and identify significant hosts within it Work with operations to develop defenses and analysis techniques

Boar

Boar
An Autonomous Agent for Network Intrusion Detection Analysis

by Archana Perumal

  • Publisher : Unknown Publisher
  • Release : 2004
  • Pages : 82
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Network Traffic Anomaly Detection and Prevention

Network Traffic Anomaly Detection and Prevention
Concepts, Techniques, and Tools

by Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita

  • Publisher : Springer
  • Release : 2017-09-03
  • Pages : 263
  • ISBN : 3319651889
  • Language : En, Es, Fr & De
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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 Intrusion Detection and Prevention

Network Intrusion Detection and Prevention
Concepts and Techniques

by Ali A. Ghorbani,Wei Lu,Mahbod Tavallaee

  • Publisher : Springer Science & Business Media
  • Release : 2009-10-10
  • Pages : 216
  • ISBN : 0387887717
  • Language : En, Es, Fr & De
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Network Intrusion Detection and Prevention: Concepts and Techniques provides detailed and concise information on different types of attacks, theoretical foundation of attack detection approaches, implementation, data collection, evaluation, and intrusion response. Additionally, it provides an overview of some of the commercially/publicly available intrusion detection and response systems. On the topic of intrusion detection system it is impossible to include everything there is to say on all subjects. However, we have tried to cover the most important and common ones. Network Intrusion Detection and Prevention: Concepts and Techniques is designed for researchers and practitioners in industry. This book is suitable for advanced-level students in computer science as a reference book as well.

Anomaly-based Network Intrusion Detection Using Outlier Subspace Analysis Approach

Anomaly-based Network Intrusion Detection Using Outlier Subspace Analysis Approach
A Book

by David Kershaw

  • Publisher : Unknown Publisher
  • Release : 2010
  • Pages : 172
  • ISBN : 9780494636398
  • Language : En, Es, Fr & De
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Guide to Computer Network Security

Guide to Computer Network Security
A Book

by Joseph Migga Kizza

  • Publisher : Springer Nature
  • Release : 2020-06-03
  • Pages : 595
  • ISBN : 3030381412
  • Language : En, Es, Fr & De
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This timely textbook presents a comprehensive guide to the core topics in cybersecurity, covering issues of security that extend beyond traditional computer networks to the ubiquitous mobile communications and online social networks that have become part of our daily lives. In the context of our growing dependence on an ever-changing digital ecosystem, this book stresses the importance of security awareness, whether in our homes, our businesses, or our public spaces. This fully updated new edition features new material on the security issues raised by blockchain technology, and its use in logistics, digital ledgers, payments systems, and digital contracts. Topics and features: Explores the full range of security risks and vulnerabilities in all connected digital systems Inspires debate over future developments and improvements necessary to enhance the security of personal, public, and private enterprise systems Raises thought-provoking questions regarding legislative, legal, social, technical, and ethical challenges, such as the tension between privacy and security Describes the fundamentals of traditional computer network security, and common threats to security Reviews the current landscape of tools, algorithms, and professional best practices in use to maintain security of digital systems Discusses the security issues introduced by the latest generation of network technologies, including mobile systems, cloud computing, and blockchain Presents exercises of varying levels of difficulty at the end of each chapter, and concludes with a diverse selection of practical projects Offers supplementary material for students and instructors at an associated website, including slides, additional projects, and syllabus suggestions This important textbook/reference is an invaluable resource for students of computer science, engineering, and information management, as well as for practitioners working in data- and information-intensive industries.

Analysis of Theoretical and Applied Machine Learning Models for Network Intrusion Detection

Analysis of Theoretical and Applied Machine Learning Models for Network Intrusion Detection
A Book

by Jonah Baron

  • Publisher : Unknown Publisher
  • Release : 2021
  • Pages : 272
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Network Intrusion Detection Systems (IDS) devices play a crucial role in the realm of network security. ... Machine learning models can be trained using a combination of machine learning algorithms, network intrusion datasets, and optimization techniques. This study sought to identify which variation of these parameters yielded the best-performing network intrusion detection models, measured by their accuracy, precision, recall, and F1 score metrics. Additionally, this research aimed to validate theoretical models' metrics by applying them in a real-world environment to see if they perform as expected.

On the Application of Locality to Network Intrusion Detection

On the Application of Locality to Network Intrusion Detection
Working-set Analysis of Real and Synthetic Network Server Traffic

by Robert Lee

  • Publisher : Unknown Publisher
  • Release : 2009
  • Pages : 119
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Keeping computer networks safe from attack requires ever-increasing vigilance. Our work on applying locality to network intrusion detection is presented in this dissertation. Network servers that allow connections from both the internal network and the Internet are vulnerable to attack from all sides. Analysis of the behavior of incoming connections for properties of locality can be used to create a normal profile for such network servers. Intrusions can then be detected due to their abnormal behavior. Data was collected from a typical network server both under normal conditions and under specific attacks. Experiments show that connections to the server do in fact exhibit locality, and attacks on the server can be detected through their violation of locality. Key to the detection of locality is a data structure called a working-set, which is a kind of cache of certain data related to network connections. Under real network conditions, we have demonstrated that the working-set behaves in a manner consistent with locality. Determining the reasons for this behavior is our next goal. A model that generates synthetic traffic based on actual network traffic allows us to study basic traffic characteristics. Simulation of working-set processing of the synthetic traffic shows that it behaves much like actual traffic. Attacks inserted into a replay of the synthetic traffic produce working-set responses similar to those produced in actual traffic. In the future, our model can be used to further the development of intrusion detection strategies.

Wireless Network Intrusion Detection and Analysis Using Federated Learning

Wireless Network Intrusion Detection and Analysis Using Federated Learning
A Book

by Burak Cetin

  • Publisher : Unknown Publisher
  • Release : 2020
  • Pages : 34
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Wi-Fi has become the wireless networking standard that allows short-to medium-range devices to connect without wires. For the last 20 years, the Wi-Fi technology has been so pervasive that most devices in use today are mobile and connect to the internet through Wi-Fi. Unlike wired network, a wireless network lacks a clear boundary, which leads to significant Wi-Fi network security concerns, especially because the current security measures are prone to several types of intrusion. To address this problem, machine learning and deep learning methods have been successfully developed to identify network attacks. However, collecting data to develop models is expensive and raises privacy concerns. The goal of this thesis is to evaluate a federated learning approach that would alleviate such privacy concerns. This work on intrusion detection is performed in a simulated environment. During the work, different experiments have concluded to define points that can affect the accuracy of a model to allow edge devices to collaboratively update global anomaly detection models using a privacy-aware approach. Three comparison tests were done in order to find the optimal results; different training rates, different training methods, different parameters. Using different combinations of 5 parameters - training algorithms, number of epochs, devices per round, round numbers and size of the sample set-, these tests with the AWID intrusion detection data set, show that our federated approach is effective in terms of classification accuracy (with an accuracy range of 88-95%), computation cost, as well as communication cost. In our study, the best case had the most rounds, epoch and the devices per round compared to the others.

Computer Network Security

Computer Network Security
A Book

by Joseph Migga Kizza

  • Publisher : Springer Science & Business Media
  • Release : 2005-12-05
  • Pages : 535
  • ISBN : 0387252282
  • Language : En, Es, Fr & De
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A comprehensive survey of computer network security concepts, methods, and practices. This authoritative volume provides an optimal description of the principles and applications of computer network security in particular, and cyberspace security in general. The book is thematically divided into three segments: Part I describes the operation and security conditions surrounding computer networks; Part II builds from there and exposes readers to the prevailing security situation based on a constant security threat; and Part III - the core - presents readers with most of the best practices and solutions currently in use. It is intended as both a teaching tool and reference. This broad-ranging text/reference comprehensively surveys computer network security concepts, methods, and practices and covers network security tools, policies, and administrative goals in an integrated manner. It is an essential security resource for undergraduate or graduate study, practitioners in networks, and professionals who develop and maintain secure computer network systems.

Network Security Through Data Analysis

Network Security Through Data Analysis
Building Situational Awareness

by Michael Collins

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2014-02-10
  • Pages : 348
  • ISBN : 1449357881
  • Language : En, Es, Fr & De
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Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In this practical guide, security researcher Michael Collins shows you several techniques and tools for collecting and analyzing network traffic datasets. You’ll understand how your network is used, and what actions are necessary to protect and improve it. Divided into three sections, this book examines the process of collecting and organizing data, various tools for analysis, and several different analytic scenarios and techniques. It’s ideal for network administrators and operational security analysts familiar with scripting. Explore network, host, and service sensors for capturing security data Store data traffic with relational databases, graph databases, Redis, and Hadoop Use SiLK, the R language, and other tools for analysis and visualization Detect unusual phenomena through Exploratory Data Analysis (EDA) Identify significant structures in networks with graph analysis Determine the traffic that’s crossing service ports in a network Examine traffic volume and behavior to spot DDoS and database raids Get a step-by-step process for network mapping and inventory

Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection

Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1998
  • Pages : 65
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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All currently available network intrusion detection (ID) systems rely upon a mechanism of data collection passive protocol analysis-which is fundamentally flawed. In passive protocol analysis, the intrusion detection system (IDS) unobtrusively watches all traffic on the network, and scrutinizes it for patterns of suspicious activity. We outline in this paper two basic problems with the reliability of passive protocol analysis: (1) there isn't enough information on the wire on which to base conclusions about what is actually happening on networked machines, and (2) the fact that the system is passive makes it inherently "fail-open," meaning that a compromise in the availability of the IDS doesn't compromise the availability of the network. We define three classes of attacks which exploit these fundamentally problems--insertion, evasion and denial of service attacks--and describe how to apply these three types of attacks to IP and TCP protocol analysis. We present the results of tests of the efficacy of our attacks against four of the most popular network intrusion detection systems on the market. All of the ID systems tested were found to be vulnerable to each of our attacks. This indicates that network ID systems cannot be fully trusted until they are fundamentally redesigned.

Analysis and Evaluation of Network Intrusion Detection Methods to Uncover Data Theft

Analysis and Evaluation of Network Intrusion Detection Methods to Uncover Data Theft
A Book

by Julien Corsini

  • Publisher : Unknown Publisher
  • Release : 2009
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Nowadays, the majority of corporations mainly use signature-based intrusion detection. This trend is partly due to the fact that signature detection is a well-known technology, as opposed to anomaly detection which is one of the hot topics in network security research. A second reason for this fact may be that anomaly detectors are known to generate many alerts, the majority of which being false alarms. Corporations need concrete comparisons between different tools in order to choose which is best suited for their needs. This thesis aims at comparing an anomaly detector with a signature detector in order to establish which is best suited to detect a data theft threat. The second aim of this thesis is to establish the influence of the training period length of an anomaly Intrusion Detection System (IDS) on its detection rate. This thesis presents a Network-based Intrusion Detection System (NIDS) evaluation testbed setup. It shows the setup of two IDSes, the signature detector Snort and the anomaly detector Statistical Packet Anomaly Detection Engine (SPADE). The evaluation testbed also includes the setup of a data theft scenario (reconnaissance, brute force attack on server and data theft). The results from the experiments carried out in this thesis proved inconclusive, mainly due to the fact that the anomaly detector SPADE requires a configuration adapted to the network monitored. Despite the fact that the experimental results proved inconclusive, this thesis could act as documentation for setting up a NIDS evaluation testbed. It could also be considered as documentation for the anomaly detector SPADE. This statement is made from the observation that there is no centralised documentation about SPADE, and not a single research paper documents the setup of an evaluation testbed.

Modeling, Design and Performance Analysis of Firewall Switch for High Speed ATM Networks

Modeling, Design and Performance Analysis of Firewall Switch for High Speed ATM Networks
A Book

by Dereje Yohannes Ashenafi

  • Publisher : Dereje Yohannes
  • Release : 2011
  • Pages : 170
  • ISBN : 9994499564
  • Language : En, Es, Fr & De
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Network Traffic Anomaly Detection and Prevention

Network Traffic Anomaly Detection and Prevention
Concepts, Techniques, and Tools

by Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita

  • Publisher : Springer
  • Release : 2017-09-19
  • Pages : 263
  • ISBN : 9783319651866
  • Language : En, Es, Fr & De
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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.