[PDF] Anomaly Detection And Health Analysis Techniques For Core Router Systems eBook

Author : Shi Jin
Publisher : Springer Nature
Page : 155 pages
File Size : 36,90 MB
Release : 2019-12-19
Category : Technology & Engineering
ISBN : 3030336646

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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 : Monowar H. Bhuyan
Publisher : Springer
Page : 278 pages
File Size : 34,52 MB
Release : 2017-09-03
Category : Computers
ISBN : 3319651889

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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.

Author : Dhruba Kumar Bhattacharyya
Publisher : CRC Press
Page : 364 pages
File Size : 49,36 MB
Release : 2013-06-18
Category : Computers
ISBN : 146658209X

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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

Author : IEEE Staff
Publisher :
Page : pages
File Size : 38,80 MB
Release : 2018-11-05
Category :
ISBN : 9781538675021

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ICCAD serves EDA and design professionals, highlighting new challenges and innovative solutions for integrated circuit design technology and systems

Author : Zahra Zohrevand
Publisher :
Page : 160 pages
File Size : 22,95 MB
Release : 2020
Category :
ISBN :

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Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health.

Author : Mastorakis, George
Publisher : IGI Global
Page : 460 pages
File Size : 38,81 MB
Release : 2015-03-31
Category : Computers
ISBN : 1466682264

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As more and more of our data is stored remotely, accessing that data wherever and whenever it is needed is a critical concern. More concerning is managing the databanks and storage space necessary to enable cloud systems. Resource Management of Mobile Cloud Computing Networks and Environments reports on the latest advances in the development of computationally intensive and cloud-based applications. Covering a wide range of problems, solutions, and perspectives, this book is a scholarly resource for specialists and end-users alike making use of the latest cloud technologies.

Author : Imtiaz Ullah
Publisher :
Page : 0 pages
File Size : 34,36 MB
Release : 2022
Category :
ISBN :

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Computer systems have become an integral part of our daily lives. The Internet of Things (IoT) has recently attracted considerable attention in the information technology industry due to its various benefits. IoT activities increase the quantity of information shared. It produces new services through the Internet due to advancements in information and communication technology. The growing development of IoT devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. These attacks have effectively accomplished malicious goals because intruders use novel and innovative techniques to conduct cyber-attacks. The security of IoT networks is becoming increasingly challenging, and anomaly detection for IoT networks is a critical technique for addressing this issue. The security challenge is to develop techniques to identify malicious activity correctly, mitigate the impact of such activity, and utilize them to implement enhanced Intrusion Detection Systems (IDS) to detect novel trends of cyber-attacks. Anomaly-based IDSs that use machine learning methods can detect and classify anomalies in IoT networks. This thesis design a framework for anomalous activity analysis for intrusion detection with applications to IoT networks. Anomaly detection frameworks based on nonparametric machine learning methods, feed-forward neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks have been designed. A technique for creating a new dataset from existing pcap files has been described. The proposed technique created five IoT network intrusion datasets from existing pcap files. A method for identifying IoT devices connected to a network using machine learning has been proposed. Two datasets were generated for IoT device identification utilizing preexisting pcap files. The generated datasets are publicly available. The performance of anomalous activity analysis frameworks was evaluated and tested in binary and multiclass classification environments using four network intrusion datasets and five IoT network intrusion datasets. In each evaluative situation, the frameworks in this thesis improve the benchmark techniques in terms of accuracy, precision, recall, and F1 score.

Author : G. Nagappan
Publisher : CRC Press
Page : 298 pages
File Size : 29,50 MB
Release : 2024-06-07
Category : Computers
ISBN : 1040130348

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We feel greatly honoured to have been assigned the job of organizing the AICTE Sponsored International Conference on Application of AI, ML, DL, Big Data on Recent Societal Issues (ICARSI’2023) on April 21 & April 22,2023 at Saveetha Engineering College. The international conference is a platform that brings together the brightest minds from across the globe to share their ideas and insights on the recent societal issues with Artificial intelligence, Machine Learning, Deep Learning, Big data and emerging technologies. With an aim to promote collaboration and foster innovation, this conference promises to be a melting pot of ideas and knowledge sharing.

Author : Nawaz Mohamudally
Publisher : IntechOpen
Page : 182 pages
File Size : 50,27 MB
Release : 2018-01-24
Category : Computers
ISBN : 9535137425

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Time Series Analysis (TSA) and Applications offers a dense content of current research and development in the field of data science. The book presents time series from a multidisciplinary approach that covers a wide range of sectors ranging from biostatistics to renewable energy forecasting. Contrary to previous literatures on time, serious readers will discover the potential of TSA in areas other than finance or weather forecasting. The choice of the algorithmic transform for different scenarios, which is a key determinant in the application of TSA, can be understood through the diverse domain applications. Readers looking for deep understanding and practicability of TSA will be delighted. Early career researchers too will appreciate the technicalities and refined mathematical complexities surrounding TSA. Our wish is that this book adds to the body of TSA knowledge and opens up avenues for those who are looking forward to applying TSA in their own context.

Author : David Loshin
Publisher : Elsevier
Page : 423 pages
File Size : 18,1 MB
Release : 2010-11-22
Category : Computers
ISBN : 0080920349

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The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.