Traffic Anomaly Detection

Traffic Anomaly Detection
Author: Antonio Cuadra-Sánchez,Javier Aracil
Publsiher: Elsevier
Total Pages: 70
Release: 2015-10-31
ISBN 10: 0081008074
ISBN 13: 9780081008072
Language: EN, FR, DE, ES & NL

Traffic Anomaly Detection Book Review:

This book presents an overview of traffic anomaly detection analysis, allowing you to monitor security aspects of multimedia services. The author's approach is based on the analysis of time aggregation adjacent periods of the traffic. As traffic varies throughout the day, it is essential to consider the concrete traffic period in which the anomaly occurs. This book presents the algorithms proposed specifically for this analysis and an empirical comparative analysis of those methods and settle a new information theory based technique, named "typical day analysis". A new information-theory based technique for traffic anomaly detection (typical day analysis) Introductory chapters to anomaly detection methods including control charts, tests of goodness-of-fit Mutual Information Contains comparative analysis of traffic anomaly detection methods

Network Traffic Anomaly Detection and Prevention

Network Traffic Anomaly Detection and Prevention
Author: Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita
Publsiher: Springer
Total Pages: 263
Release: 2018-08-12
ISBN 10: 9783319879680
ISBN 13: 3319879685
Language: EN, FR, DE, ES & NL

Network Traffic Anomaly Detection and Prevention Book Review:

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 Traffic Anomaly Detection and Prevention

Network Traffic Anomaly Detection and Prevention
Author: Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita
Publsiher: Springer
Total Pages: 263
Release: 2017-09-03
ISBN 10: 3319651889
ISBN 13: 9783319651880
Language: EN, FR, DE, ES & NL

Network Traffic Anomaly Detection and Prevention Book Review:

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 Traffic Anomaly Detection and Evaluation

Network Traffic Anomaly Detection and Evaluation
Author: Daniela Brauckhoff
Publsiher: Unknown
Total Pages: 180
Release: 2010
ISBN 10: 9783832289775
ISBN 13: 3832289771
Language: EN, FR, DE, ES & NL

Network Traffic Anomaly Detection and Evaluation Book Review:

Traffic Anomaly Detection and Diagnosis on the Network Flow Level

Traffic Anomaly Detection and Diagnosis on the Network Flow Level
Author: Marc Philippe Stöcklin
Publsiher: Unknown
Total Pages: 183
Release: 2011
ISBN 10:
ISBN 13: OCLC:732346065
Language: EN, FR, DE, ES & NL

Traffic Anomaly Detection and Diagnosis on the Network Flow Level Book Review:

New Methods for Network Traffic Anomaly Detection

New Methods for Network Traffic Anomaly Detection
Author: Tahereh Tara Babaie
Publsiher: Unknown
Total Pages: 329
Release: 2014
ISBN 10:
ISBN 13: OCLC:911222015
Language: EN, FR, DE, ES & NL

New Methods for Network Traffic Anomaly Detection Book Review:

HTTP Traffic Anomaly Detection Using Clustering

HTTP Traffic Anomaly Detection Using Clustering
Author: Douglas E. Stetson
Publsiher: Unknown
Total Pages: 118
Release: 2005
ISBN 10:
ISBN 13: OCLC:190835232
Language: EN, FR, DE, ES & NL

HTTP Traffic Anomaly Detection Using Clustering Book Review:

Network Anomaly Detection

Network Anomaly Detection
Author: Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publsiher: CRC Press
Total Pages: 366
Release: 2013-06-18
ISBN 10: 1466582081
ISBN 13: 9781466582088
Language: EN, FR, DE, ES & NL

Network Anomaly Detection Book Review:

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.

A New Approach to Network Traffic Anomaly Detection

A New Approach to Network Traffic Anomaly Detection
Author: Wanchalearm Petsuwan
Publsiher: Unknown
Total Pages: 458
Release: 2010
ISBN 10:
ISBN 13: OCLC:658853720
Language: EN, FR, DE, ES & NL

A New Approach to Network Traffic Anomaly Detection Book Review:

Traffic Anomaly Detection and Cause Identification Using Flow level Measurements

Traffic Anomaly Detection and Cause Identification Using Flow level Measurements
Author: Gerhard Münz
Publsiher: Unknown
Total Pages: 228
Release: 2010
ISBN 10: 9783937201122
ISBN 13: 3937201122
Language: EN, FR, DE, ES & NL

Traffic Anomaly Detection and Cause Identification Using Flow level Measurements Book Review:

Data Traffic Monitoring and Analysis

Data Traffic Monitoring and Analysis
Author: Ernst Biersack,Christian Callegari,Maja Matijasevic
Publsiher: Springer
Total Pages: 359
Release: 2013-03-02
ISBN 10: 3642367844
ISBN 13: 9783642367847
Language: EN, FR, DE, ES & NL

Data Traffic Monitoring and Analysis Book Review:

This book was prepared as the Final Publication of COST Action IC0703 "Data Traffic Monitoring and Analysis: theory, techniques, tools and applications for the future networks". It contains 14 chapters which demonstrate the results, quality,and the impact of European research in the field of TMA in line with the scientific objective of the Action. The book is structured into three parts: network and topology measurement and modelling, traffic classification and anomaly detection, quality of experience.

Network Traffic Anomaly Detection Using EMD and Hilbert Huan Transform

Network Traffic Anomaly Detection Using EMD and Hilbert Huan Transform
Author: Jieying Han
Publsiher: Unknown
Total Pages: 168
Release: 2013
ISBN 10:
ISBN 13: OCLC:854761870
Language: EN, FR, DE, ES & NL

Network Traffic Anomaly Detection Using EMD and Hilbert Huan Transform Book Review:

Traffic Anomaly Detection Based on a Heat Diffusion Model

Traffic Anomaly Detection Based on a Heat Diffusion Model
Author: 黃暐傑
Publsiher: Unknown
Total Pages: 329
Release: 2018
ISBN 10:
ISBN 13: OCLC:1053438534
Language: EN, FR, DE, ES & NL

Traffic Anomaly Detection Based on a Heat Diffusion Model Book Review:

Recent Advances in Intrusion Detection

Recent Advances in Intrusion Detection
Author: Robin Sommer,Davide Balzarotti,Gregor Maier
Publsiher: Springer
Total Pages: 399
Release: 2012-02-11
ISBN 10: 3642236448
ISBN 13: 9783642236440
Language: EN, FR, DE, ES & NL

Recent Advances in Intrusion Detection Book Review:

This book constitutes the proceedings of the 14th International Symposium on Recent Advances in Intrusion Detection, RAID 2011, held in Menlo Park, CA, USA in September 2011. The 20 papers presented were carefully reviewed and selected from 87 submissions. The papers are organized in topical sections on application security; malware; anomaly detection; Web security and social networks; and sandboxing and embedded environments.

Network Traffic Anomaly Detection Using Modified Hidden Markov Model

Network Traffic Anomaly Detection Using Modified Hidden Markov Model
Author: Anonim
Publsiher: Unknown
Total Pages: 164
Release: 2019
ISBN 10:
ISBN 13: OCLC:1140213523
Language: EN, FR, DE, ES & NL

Network Traffic Anomaly Detection Using Modified Hidden Markov Model Book Review:

With the growing number of attacks and malicious threats on the Internet services and network infrastructures, the need for techniques to identity and detect attacks is increasing. Therefore, using machine learning techniques along traditional security mechanisms such as firewall and cryptography, can improve the performance of intrusion detection systems (IDSs). Network anomaly detection has become a very important area for both industrial application and academic research in the recent years. It is involved widely in a broad spectrum of domains and many research areas. Detection anomalies (attacks are detected as anomalies) in data is a crucial problem to diverse real-world applications. The goal of anomaly detection is to identify anomalous behavior, events based on deviations from expected normal usage. Hidden Markov Models (HMM) have been applied to anomaly detection since 1996. The previous researches applying HMM were limited to small data sets. In our work, we have used the term anomaly detection to describe the process of differentiating abnormal behavior from normal behavior on datasets available in this study. In this dissertation, we describe our research contributions for detecting anomalous patterns in network traffic data using HMM. We built HMM correlates the observation sequences and state transitions to predict the most probable intrusion state sequences that are capable of reducing false positive rate.

Combining Statistical and Spectral Analysis Techniques in Network Traffic Anomaly Detection

Combining Statistical and Spectral Analysis Techniques in Network Traffic Anomaly Detection
Author: Stevan Novakov,Carleton University. Dissertation. Engineering, Electrical and Computer
Publsiher: Unknown
Total Pages: 252
Release: 2012
ISBN 10:
ISBN 13: OCLC:811594012
Language: EN, FR, DE, ES & NL

Combining Statistical and Spectral Analysis Techniques in Network Traffic Anomaly Detection Book Review:

Anomaly Detection in Network Traffic

Anomaly Detection in Network Traffic
Author: Anup Bhange,Manmeet Kaur Marhas
Publsiher: LAP Lambert Academic Publishing
Total Pages: 92
Release: 2012
ISBN 10: 9783659297632
ISBN 13: 3659297631
Language: EN, FR, DE, ES & NL

Anomaly Detection in Network Traffic Book Review:

A worldwide Internet usage growth rate of 380% larger than the period from 2000, the year of the dot-com bubble burst, until present indicates that Internet technology has become a foundation of our daily life. In the same period, cyber-crime has seen an incredible that makes sophisticated protection device for computers and networks an absolute necessity. Firewalls as the major defense of the last decade do not give sufficient protection anymore. This fact has given rise to the expansion of intrusion detection and prevention systems. Traditional intrusion detection systems are hasty in the sense that they use a set of signatures, which raise at the same rate as new technique are discovered, to identify malicious traffic patterns. Anomaly detection systems are another branch of intrusion detection systems that act more proactively. They get a model of the normal system performance and issue alerts whenever the behavior changes; making an appropriate assumption that such changes are frequently caused by malicious or disruptive events. Anomaly detection has been a ground of exhaustive research over the last years as it poses several challenging problems.

MARITIME TRAFFIC ANOMALY DETECTION FROM AIS SATELLITE DATA IN NEAR PORT REGIONS

MARITIME TRAFFIC ANOMALY DETECTION FROM AIS SATELLITE DATA IN NEAR PORT REGIONS
Author: Bo Liu
Publsiher: Unknown
Total Pages: 329
Release: 2015
ISBN 10:
ISBN 13: OCLC:1033227487
Language: EN, FR, DE, ES & NL

MARITIME TRAFFIC ANOMALY DETECTION FROM AIS SATELLITE DATA IN NEAR PORT REGIONS Book Review:

Real time Analysis of Aggregate Network Traffic for Anomaly Detection

Real time Analysis of Aggregate Network Traffic for Anomaly Detection
Author: Seong Soo Kim
Publsiher: Unknown
Total Pages: 329
Release: 2005
ISBN 10:
ISBN 13: OCLC:61857222
Language: EN, FR, DE, ES & NL

Real time Analysis of Aggregate Network Traffic for Anomaly Detection Book Review:

The frequent and large-scale network attacks have led to an increased need for developing techniques for analyzing network traffic. If efficient analysis tools were available, it could become possible to detect the attacks, anomalies and to appropriately take action to contain the attacks before they have had time to propagate across the network. In this dissertation, we suggest a technique for traffic anomaly detection based on analyzing the correlation of destination IP addresses and distribution of image-based signal in postmortem and real-time, by passively monitoring packet headers of traffic. This address correlation data are transformed using discrete wavelet transform for effective detection of anomalies through statistical analysis. Results from trace-driven evaluation suggest that the proposed approach could provide an effective means of detecting anomalies close to the source. We present a multidimensional indicator using the correlation of port numbers as a means of detecting anomalies. We also present a network measurement approach that can simultaneously detect, identify and visualize attacks and anomalous traffic in real-time. We propose to represent samples of network packet header data as frames or images. With such a formulation, a series of samples can be seen as a sequence of frames or video. This enables techniques from image processing and video compression such as DCT to be applied to the packet header data to reveal interesting properties of traffic. We show that "scene change analysis" can reveal sudden changes in traffic behavior or anomalies. We show that "motion prediction" techniques can be employed to understand the patterns of some of the attacks. We show that it may be feasible to represent multiple pieces of data as different colors of an image enabling a uniform treatment of multidimensional packet header data. Measurement-based techniques for analyzing network traffic treat traffic volume and traffic header data as signals or images in order to make the analysis feasible. In this dissertation, we propose an approach based on the classical Neyman-Pearson Test employed in signal detection theory to evaluate these different strategies. We use both of analytical models and trace-driven experiments for comparing the performance of different strategies. Our evaluations on real traces reveal differences in the effectiveness of different traffic header data as potential signals for traffic analysis in terms of their detection rates and false alarm rates. Our results show that address distributions and number of flows are better signals than traffic volume for anomaly detection. Our results also show that sometimes statistical techniques can be more effective than the NP-test when the attack patterns change over time.

Traffic Management and Traffic Engineering for the Future Internet

Traffic Management and Traffic Engineering for the Future Internet
Author: Rui Valadas,Paulo Salvador
Publsiher: Springer Science & Business Media
Total Pages: 231
Release: 2009-09-23
ISBN 10: 3642045758
ISBN 13: 9783642045752
Language: EN, FR, DE, ES & NL

Traffic Management and Traffic Engineering for the Future Internet Book Review:

This post proeedings volume contains a selection of research contributions presented at FITraMEn 2008, held during December 11-12, 2008 in Porto, Portugal. The papers contained in this book provide a general view of the ongoing research on traffic management and traffic engineering in the Euro-NF Network of Excellence, and give a representative example of the problems currently investigated in this area, that spans topics such as bandwidth allocation and traffic control, statistical analysis, traffic engineering, and optical networks and video communications.