Data Analysis for Network Cyber Security

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 ...

Data Analysis for Network Cyber Security

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

Big Data Analytics in Cybersecurity

Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research.

Big Data Analytics in Cybersecurity

Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging analytical technology, offers the capability to collect, store, process, and visualize these vast amounts of data. Big Data Analytics in Cybersecurity examines security challenges surrounding big data and provides actionable insights that can be used to improve the current practices of network operators and administrators. Applying big data analytics in cybersecurity is critical. By exploiting data from the networks and computers, analysts can discover useful network information from data. Decision makers can make more informative decisions by using this analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes. Bringing together experts from academia, government laboratories, and industry, the book provides insight to both new and more experienced security professionals, as well as data analytics professionals who have varying levels of cybersecurity expertise. It covers a wide range of topics in cybersecurity, which include: Network forensics Threat analysis Vulnerability assessment Visualization Cyber training. In addition, emerging security domains such as the IoT, cloud computing, fog computing, mobile computing, and cyber-social networks are examined. The book first focuses on how big data analytics can be used in different aspects of cybersecurity including network forensics, root-cause analysis, and security training. Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research.

Data Science For Cyber security

This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences.

Data Science For Cyber security

Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.

Data Warehousing and Data Mining Techniques for Cyber Security

Data Warehousing and Data Mining Techniques for Cyber Security is designed for practitioners and researchers in industry. This book is also suitable for upper-undergraduate and graduate-level students in computer science.

Data Warehousing and Data Mining Techniques for Cyber Security

The application of data warehousing and data mining techniques to computer security is an important emerging area, as information processing and internet accessibility costs decline and more and more organizations become vulnerable to cyber attacks. These security breaches include attacks on single computers, computer networks, wireless networks, databases, or authentication compromises. This book describes data warehousing and data mining techniques that can be used to detect attacks. It is designed to be a useful handbook for practitioners and researchers in industry, and is also suitable as a text for advanced-level students in computer science.

Dynamic Networks and Cyber Security

However, the challenge of detecting and mitigating malicious or unauthorised behaviour through the lens of such data is becoming an increasingly prominent issue.This collection of papers by leading researchers and practitioners synthesises ...

Dynamic Networks and Cyber Security

As an under-studied area of academic research, the analysis of computer network traffic data is still in its infancy. However, the challenge of detecting and mitigating malicious or unauthorised behaviour through the lens of such data is becoming an increasingly prominent issue. This collection of papers by leading researchers and practitioners synthesises cutting-edge work in the analysis of dynamic networks and statistical aspects of cyber security. The book is structured in such a way as to keep security application at the forefront of discussions. It offers readers easy access into the area of data analysis for complex cyber-security applications, with a particular focus on temporal and network aspects. Chapters can be read as standalone sections and provide rich reviews of the latest research within the field of cyber-security. Academic readers will benefit from state-of-the-art descriptions of new methodologies and their extension to real practical problems while industry professionals will appreciate access to more advanced methodology than ever before. Contents:Network Attacks and the Data They Affect (M Morgan, J Sexton, J Neil, A Ricciardi & J Theimer)Cyber-Security Data Sources for Dynamic Network Research (A D Kent)Modelling User Behaviour in a Network Using Computer Event Logs (M J M Turcotte, N A Heard & A D Kent)Network Services as Risk Factors: A Genetic Epidemiology Approach to Cyber-Security (S Gil)Community Detection and Role Identification in Directed Networks: Understanding the Twitter Network of the Care.Data Debate (B Amor, S Vuik, R Callahan, A Darzi, S N Yaliraki & M Barahona)Anomaly Detection for Cyber Security Applications (P Rubin-Delanchy, D J Lawson & N A Heard)Exponential Random Graph Modelling of Static and Dynamic Social Networks (A Caimo)Hierarchical Dynamic Walks (A V Mantzaris, P Grindrod & D J Higham)Temporal Reachability in Dynamic Networks (A Hagberg, N Lemons & S Misra) Readership: Researchers and practitioners in dynamic network analysis and cyber-security. Key Features:Detailed descriptions of the behaviour of attackersDiscussions of new public domain data sources, including data quality issuesA collection of papers introducing novel methodology for cyber-data analysis

The Cyber Security Network Guide

This book presents a unique, step-by-step approach for monitoring, detecting, analyzing and mitigating complex network cyber threats.

The Cyber Security Network Guide


Agriculture Rural Development Food and Drug Administration and Related Agencies Appropriations for 2014

ARS will : Establish a scalable common infrastructure that includes
communication networks , cyber security ... site data coordination personnel
identified above , and software engineering expertise to enable synthetic data
analysis including ...

Agriculture  Rural Development  Food and Drug Administration  and Related Agencies Appropriations for 2014


Data Science in Cybersecurity and Cyberthreat Intelligence

This book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to ...

Data Science in Cybersecurity and Cyberthreat Intelligence

This book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms.

Advances in Data Science Cyber Security and IT Applications

This book constitutes the refereed proceedings of the First International Conference on Intelligent Cloud Computing, ICC 2019, held in Riyadh, Saudi Arabia, in December 2019.

Advances in Data Science  Cyber Security and IT Applications

This book constitutes the refereed proceedings of the First International Conference on Intelligent Cloud Computing, ICC 2019, held in Riyadh, Saudi Arabia, in December 2019. The two-volume set presents 53 full papers, which were carefully reviewed and selected from 174 submissions. The papers are organized in topical sections on Cyber Security; Data Science; Information Technology and Applications; Network and IoT.

Cyber Security Intelligence and Analytics

This book presents the outcomes of the 2020 International Conference on Cyber Security Intelligence and Analytics (CSIA 2020), which was dedicated to promoting novel theoretical and applied research advances in the interdisciplinary field ...

Cyber Security Intelligence and Analytics

This book presents the outcomes of the 2020 International Conference on Cyber Security Intelligence and Analytics (CSIA 2020), which was dedicated to promoting novel theoretical and applied research advances in the interdisciplinary field of cyber security, particularly those focusing on threat intelligence, analytics, and preventing cyber crime. The conference provides a forum for presenting and discussing innovative ideas, cutting-edge research findings, and novel techniques, methods, and applications concerning all aspects of cyber security intelligence and analytics. CSIA 2020, which was held in Haikou, China on February 28–29, 2020, built on the previous conference in Wuhu, China (2019), and marks the series’ second successful installment.

Cybersecurity Analytics

Trying to build a malware detector, a phishing email detector, or just interested in finding patterns in your datasets? This book can let you do it on your own.

Cybersecurity Analytics

Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware detector, a phishing email detector, or just interested in finding patterns in your datasets? This book can let you do it on your own. Numerous examples and datasets links are included so that the reader can "learn by doing." Anyone with a basic college-level calculus course and some probability knowledge can easily understand most of the material. The book includes chapters containing: unsupervised learning, semi-supervised learning, supervised learning, text mining, natural language processing, and more. It also includes background on security, statistics, and linear algebra. The website for the book contains a listing of datasets, updates, and other resources for serious practitioners.

Hands On Machine Learning for Cybersecurity

In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries.

Hands On Machine Learning for Cybersecurity

Get into the world of smart data security using machine learning algorithms and Python libraries Key Features Learn machine learning algorithms and cybersecurity fundamentals Automate your daily workflow by applying use cases to many facets of security Implement smart machine learning solutions to detect various cybersecurity problems Book Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learn Use machine learning algorithms with complex datasets to implement cybersecurity concepts Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes Use TensorFlow in the cybersecurity domain and implement real-world examples Learn how machine learning and Python can be used in complex cyber issues Who this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

Guide to Vulnerability Analysis for Computer Networks and Systems

This professional guide and reference examines the challenges of assessing security vulnerabilities in computing infrastructure.

Guide to Vulnerability Analysis for Computer Networks and Systems

This professional guide and reference examines the challenges of assessing security vulnerabilities in computing infrastructure. Various aspects of vulnerability assessment are covered in detail, including recent advancements in reducing the requirement for expert knowledge through novel applications of artificial intelligence. The work also offers a series of case studies on how to develop and perform vulnerability assessment techniques using start-of-the-art intelligent mechanisms. Topics and features: provides tutorial activities and thought-provoking questions in each chapter, together with numerous case studies; introduces the fundamentals of vulnerability assessment, and reviews the state of the art of research in this area; discusses vulnerability assessment frameworks, including frameworks for industrial control and cloud systems; examines a range of applications that make use of artificial intelligence to enhance the vulnerability assessment processes; presents visualisation techniques that can be used to assist the vulnerability assessment process. In addition to serving the needs of security practitioners and researchers, this accessible volume is also ideal for students and instructors seeking a primer on artificial intelligence for vulnerability assessment, or a supplementary text for courses on computer security, networking, and artificial intelligence.

Data Intensive Computing

10. Data-Intensive. Visual. Analysis. for. Cyber-Security. William A. Pike, Daniel M
. Best, Douglas V. Love, and Shawn J. Bohn 10.1 Introduction Protecting
communications networks against attacks where the aim is to steal information,
disrupt ...

Data Intensive Computing

Describes principles of the emerging field of data-intensive computing, along with methods for designing, managing and analyzing the big data sets of today.

Cisco NetFlow for Cyber Security Big Data Analytics

Description Cisco NetFlow for Cyber Security Big Data Analytics walks you through the steps for deploying, configuring, and troubleshooting NetFlow and learning big data analytics technologies for cyber security.

Cisco NetFlow for Cyber Security Big Data Analytics

More than 6 hours of video training covering everything you need to know to deploy, configure, and troubleshoot NetFlow in many different Cisco platforms and learn big data analytics technologies for cyber security. Description Cisco NetFlow for Cyber Security Big Data Analytics walks you through the steps for deploying, configuring, and troubleshooting NetFlow and learning big data analytics technologies for cyber security. Cisco NetFlow creates an environment where network administrators and security professionals have the tools to understand who, what, when, where, and how network traffic is flowing. Cisco NetFlow LiveLessons is a key resource for understanding the power behind the Cisco NetFlow solution. Omar Santos, a Cisco Product Security Incident Response Team (PSIRT) technical leader and author of Network Security with NetFlow and IPFIX, the CCNA Security 210-260 Official Cert Guide, and other key security video and book titles by Cisco Press demonstrates how NetFlow can be used by large enterprises and small-to-medium-sized businesses to meet critical network challenges. This video courseexplores everything you need to understand and implement the Cisco Cyber Threat Defense Solution, while also providing configuration and troubleshooting walk-throughs. Skill Level Intermediate What You Will Learn NetFlow and IPFIX basics NetFlow Deployment Scenarios Cisco Flexible NetFlow NetFlow Commercial and Open Source Monitoring and Analysis Software Packages Big Data Analytics Tools The Cisco Cyber Threat Defense Solution Troubleshooting NetFlow NetFlow for Anomaly Detection and Identifying DoS Attacks NetFlow for Incident Response and Forensics Who Should Take This Course Network and security professionals interested in learning about the Cisco NetFlow solution; anyone wishing to build Cisco security About LiveLessons Video Training LiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Programming, Web Development, Mobile Development, Home and Office Technologies, Business and Management, and more. View all LiveLessons on InformIT at http:...

Network Security With Netflow and Ipfix

Cisco NetFlow can help companies of all sizes achieve and maintain this visibility.Network Security with NetFlow and IPFIX: Big Data Analytics for Information Security is the definitive guide to using NetFlow to strengthen network security.

Network Security With Netflow and Ipfix

Today, security demands unprecedented visibility into your network. Cisco NetFlow can help companies of all sizes achieve and maintain this visibility. Network Security with NetFlow and IPFIX: Big Data Analytics for Information Security is the definitive guide to using NetFlow to strengthen network security. Omar Santos, Technical Leader of Cisco's Product Security Incident Response Team (PSIRT), covers all you need to successfully capture network telemetry with NetFlow and use it to: See what is actually happening across your entire network Regain control of your network Quickly identify compromised end points and network infrastructure devices Monitor network usage by employees, contractors, or partners Detect firewall misconfigurations and inappropriate access to corporate resources Act effectively during incident response and network forensics Utilize big data analytics to improve IT security Writing for organizations of all sizes, Santos shows how to work with each current version of NetFlow, and several leading open source analyzers. He addresses NetFlow services, versions, and features; shows how to perform Big Data security analyses of Cisco NetFlow data; and explains how NetFlow integrates into broader Cisco Cyber Threat Defense (CTD) solutions. Each chapter presents multiple sample configurations, accompanied by detailed design analyses and realistic case studies.

Handbook of Computer Networks and Cyber Security

This handbook introduces the basic principles and fundamentals of cyber security towards establishing an understanding of how to protect computers from hackers and adversaries.

Handbook of Computer Networks and Cyber Security

This handbook introduces the basic principles and fundamentals of cyber security towards establishing an understanding of how to protect computers from hackers and adversaries. The highly informative subject matter of this handbook, includes various concepts, models, and terminologies along with examples and illustrations to demonstrate substantial technical details of the field. It motivates the readers to exercise better protection and defense mechanisms to deal with attackers and mitigate the situation. This handbook also outlines some of the exciting areas of future research where the existing approaches can be implemented. Exponential increase in the use of computers as a means of storing and retrieving security-intensive information, requires placement of adequate security measures to safeguard the entire computing and communication scenario. With the advent of Internet and its underlying technologies, information security aspects are becoming a prime concern towards protecting the networks and the cyber ecosystem from variety of threats, which is illustrated in this handbook. This handbook primarily targets professionals in security, privacy and trust to use and improve the reliability of businesses in a distributed manner, as well as computer scientists and software developers, who are seeking to carry out research and develop software in information and cyber security. Researchers and advanced-level students in computer science will also benefit from this reference.