Statistical Methods in Computer Security

Statistical Methods in Computer Security PDF

Author: William W.S. Chen

Publisher: CRC Press

Published: 2004-12-28

Total Pages: 377

ISBN-13: 1420030884

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Statistical Methods in Computer Security summarizes discussions held at the recent Joint Statistical Meeting to provide a clear layout of current applications in the field. This blue-ribbon reference discusses the most influential advancements in computer security policy, firewalls, and security issues related to passwords. It addresses crime and m

Statistical Methods in Computer Security

Statistical Methods in Computer Security PDF

Author: Reynaldo Ballard

Publisher: Createspace Independent Publishing Platform

Published: 2017-03-23

Total Pages: 362

ISBN-13: 9781548551841

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Statistical Methods in Computer Security summarizes discussions held at the recent Joint Statistical Meeting to provide a clear layout of current applications in the field. This blue-ribbon reference discusses the most influential advancements in computer security policy, firewalls, and security issues related to passwords. It addresses crime and misconduct on the Internet, considers the development of infrastructures that may prevent breaches of security and law, and illustrates the vulnerability of networked computers to new virus attacks despite widespread deployment of antivirus software, firewalls, and other network security equipment.

Federal Statistics, Multiple Data Sources, and Privacy Protection

Federal Statistics, Multiple Data Sources, and Privacy Protection PDF

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2018-01-27

Total Pages: 195

ISBN-13: 0309465370

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The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.

Statistical Analysis of Network Data

Statistical Analysis of Network Data PDF

Author: Eric D. Kolaczyk

Publisher: Springer Science & Business Media

Published: 2009-04-20

Total Pages: 397

ISBN-13: 0387881468

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In recent years there has been an explosion of network data – that is, measu- ments that are either of or from a system conceptualized as a network – from se- ingly all corners of science. The combination of an increasingly pervasive interest in scienti c analysis at a systems level and the ever-growing capabilities for hi- throughput data collection in various elds has fueled this trend. Researchers from biology and bioinformatics to physics, from computer science to the information sciences, and from economics to sociology are more and more engaged in the c- lection and statistical analysis of data from a network-centric perspective. Accordingly, the contributions to statistical methods and modeling in this area have come from a similarly broad spectrum of areas, often independently of each other. Many books already have been written addressing network data and network problems in speci c individual disciplines. However, there is at present no single book that provides a modern treatment of a core body of knowledge for statistical analysis of network data that cuts across the various disciplines and is organized rather according to a statistical taxonomy of tasks and techniques. This book seeks to ll that gap and, as such, it aims to contribute to a growing trend in recent years to facilitate the exchange of knowledge across the pre-existing boundaries between those disciplines that play a role in what is coming to be called ‘network science.

Computer Intensive Statistical Methods

Computer Intensive Statistical Methods PDF

Author: J. S. Urban. Hjorth

Publisher: Routledge

Published: 2017-10-19

Total Pages: 173

ISBN-13: 1351458744

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This book focuses on computer intensive statistical methods, such as validation, model selection, and bootstrap, that help overcome obstacles that could not be previously solved by methods such as regression and time series modelling in the areas of economics, meteorology, and transportation.

Cybersecurity Data Science

Cybersecurity Data Science PDF

Author: Scott Mongeau

Publisher: Springer Nature

Published: 2021-10-01

Total Pages: 410

ISBN-13: 3030748960

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This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.

Data Analysis for Network Cyber-Security

Data Analysis for Network Cyber-Security PDF

Author: Niall Adams

Publisher: World Scientific

Published: 2014-02-28

Total Pages: 200

ISBN-13: 1783263768

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There is increasing pressure to protect computer networks against unauthorized intrusion, and some work in this area is concerned with engineering systems that are robust to attack. However, no system can be made invulnerable. Data Analysis for Network Cyber-Security focuses on monitoring and analyzing network traffic data, with the intention of preventing, or quickly identifying, malicious activity. Such work involves the intersection of statistics, data mining and computer science. Fundamentally, network traffic is relational, embodying a link between devices. As such, graph analysis approaches are a natural candidate. However, such methods do not scale well to the demands of real problems, and the critical aspect of the timing of communications events is not accounted for in these approaches. This book gathers papers from leading researchers to provide both background to the problems and a description of cutting-edge methodology. The contributors are from diverse institutions and areas of expertise and were brought together at a workshop held at the University of Bristol in March 2013 to address the issues of network cyber security. The workshop was supported by the Heilbronn Institute for Mathematical Research. Contents:Inference for Graphs and Networks: Adapting Classical Tools to Modern Data (Benjamin P Olding and Patrick J Wolfe)Rapid Detection of Attacks in Computer Networks by Quickest Changepoint Detection Methods (Alexander G Tartakovsky)Statistical Detection of Intruders Within Computer Networks Using Scan Statistics (Joshua Neil, Curtis Storlie, Curtis Hash and Alex Brugh)Characterizing Dynamic Group Behavior in Social Networks for Cybernetics (Sumeet Dua and Pradeep Chowriappa)Several Approaches for Detecting Anomalies in Network Traffic Data (Céline Lévy-Leduc)Monitoring a Device in a Communication Network (Nicholas A Heard and Melissa Turcotte) Readership: Researchers and graduate students in the fields of network traffic data analysis and network cyber security. Key Features:This book is unique in being a treatise on the statistical analysis of network traffic dataThe contributors are leading researches in the field and will give authoritative descriptions of cutting edge methodologyThe book features material from diverse areas, and as such forms a unified view of network cyber securityKeywords:Network Data Analysis;Cyber Security;Change Detection;Anomaly Detection

Information Fusion for Cyber-Security Analytics

Information Fusion for Cyber-Security Analytics PDF

Author: Izzat M Alsmadi

Publisher: Springer

Published: 2016-10-21

Total Pages: 379

ISBN-13: 3319442570

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This book highlights several gaps that have not been addressed in existing cyber security research. It first discusses the recent attack prediction techniques that utilize one or more aspects of information to create attack prediction models. The second part is dedicated to new trends on information fusion and their applicability to cyber security; in particular, graph data analytics for cyber security, unwanted traffic detection and control based on trust management software defined networks, security in wireless sensor networks & their applications, and emerging trends in security system design using the concept of social behavioral biometric. The book guides the design of new commercialized tools that can be introduced to improve the accuracy of existing attack prediction models. Furthermore, the book advances the use of Knowledge-based Intrusion Detection Systems (IDS) to complement existing IDS technologies. It is aimed towards cyber security researchers.

Computer Intrusion Detection and Network Monitoring

Computer Intrusion Detection and Network Monitoring PDF

Author: David J. Marchette

Publisher: Springer Science & Business Media

Published: 2013-04-17

Total Pages: 339

ISBN-13: 1475734581

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This book covers the basic statistical and analytical techniques of computer intrusion detection. It is the first to present a data-centered approach to these problems. It begins with a description of the basics of TCP/IP, followed by chapters dealing with network traffic analysis, network monitoring for intrusion detection, host based intrusion detection, and computer viruses and other malicious code.

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