| data analysis cyber security: Big Data Analytics in Cybersecurity Onur Savas, Julia Deng, 2017-09-18 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 analysis cyber security: Cybersecurity Analytics Rakesh M. Verma, David J. Marchette, 2019-11-27 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. |
| data analysis cyber security: Data Analytics for Cybersecurity Vandana P. Janeja, 2022 As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and end users. Analyzing data across different segments of cybersecurity domains, particularly data generated during cyber-attacks, can help us understand threats better, prevent future cyber-attacks, and provide insights into the evolving cyber threat landscape. This book takes a data oriented approach to studying cyber threats, showing in depth how traditional methods such as anomaly detection can be extended using data analytics and also applies data analytics to non-traditional views of cybersecurity, such as multi domain analysis, time series and spatial data analysis, and human-centered cybersecurity-- |
| data analysis cyber security: Data Analysis For Network Cyber-security Niall M Adams, Nicholas A Heard, 2014-04-04 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. |
| data analysis cyber security: Machine Intelligence and Big Data Analytics for Cybersecurity Applications Yassine Maleh, Mohammad Shojafar, Mamoun Alazab, Youssef Baddi, 2020-12-14 This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field. Bridging an important gap between machine intelligence, big data, and cybersecurity communities, it aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances on machine intelligence and big data analytics for cybersecurity applications. |
| data analysis cyber security: Network Security Through Data Analysis Michael S Collins, 2014-02-10 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 |
| data analysis cyber security: Data-Driven Security Jay Jacobs, Bob Rudis, 2014-02-24 Uncover hidden patterns of data and respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions. Everything in this book will have practical application for information security professionals. Helps IT and security professionals understand and use data, so they can thwart attacks and understand and visualize vulnerabilities in their networks Includes more than a dozen real-world examples and hands-on exercises that demonstrate how to analyze security data and intelligence and translate that information into visualizations that make plain how to prevent attacks Covers topics such as how to acquire and prepare security data, use simple statistical methods to detect malware, predict rogue behavior, correlate security events, and more Written by a team of well-known experts in the field of security and data analysis Lock down your networks, prevent hacks, and thwart malware by improving visibility into the environment, all through the power of data and Security Using Data Analysis, Visualization, and Dashboards. |
| data analysis cyber security: Information Fusion for Cyber-Security Analytics Izzat M Alsmadi, George Karabatis, Ahmed Aleroud, 2016-10-21 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. |
| data analysis cyber security: Machine Learning Approaches in Cyber Security Analytics Tony Thomas, Athira P. Vijayaraghavan, Sabu Emmanuel, 2019-12-16 This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks. |
| data analysis cyber security: Data Science For Cyber-security Nicholas A Heard, Niall M Adams, Patrick Rubin-delanchy, Mellisa Turcotte, 2018-09-26 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 analysis cyber security: Security Analytics Mehak Khurana, Shilpa Mahajan, 2022 The book gives a comprehensive overview of security issues in cyber physical systems by examining and analyzing the vulnerabilities. It also brings current understanding of common web vulnerabilities and its analysis while maintaining awareness and knowledge of contemporary standards, practices, procedures and methods of Open Web Application Security Project. This book is a medium to funnel creative energy and develop new skills of hacking and analysis of security and expedites the learning of the basics of investigating crimes, including intrusion from the outside and damaging practices from the inside, how criminals apply across devices, networks, and the internet at large and analysis of security data. Features Helps to develop an understanding of how to acquire, prepare, visualize security data. Unfolds the unventured sides of the cyber security analytics and helps spread awareness of the new technological boons. Focuses on the analysis of latest development, challenges, ways for detection and mitigation of attacks, advanced technologies, and methodologies in this area. Designs analytical models to help detect malicious behaviour. The book provides a complete view of data analytics to the readers which include cyber security issues, analysis, threats, vulnerabilities, novel ideas, analysis of latest techniques and technology, mitigation of threats and attacks along with demonstration of practical applications, and is suitable for a wide-ranging audience from graduates to professionals/practitioners and researchers. |
| data analysis cyber security: Network Security Through Data Analysis Michael Collins, 2017-09-08 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 |
| data analysis cyber security: Malware Data Science Joshua Saxe, Hillary Sanders, 2018-09-25 Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a big data problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve. |
| data analysis cyber security: Information Security Analytics Mark Talabis, Jason Martin, Robert McPherson, Inez Miyamoto, 2014-11-25 Information Security Analytics gives you insights into the practice of analytics and, more importantly, how you can utilize analytic techniques to identify trends and outliers that may not be possible to identify using traditional security analysis techniques. Information Security Analytics dispels the myth that analytics within the information security domain is limited to just security incident and event management systems and basic network analysis. Analytic techniques can help you mine data and identify patterns and relationships in any form of security data. Using the techniques covered in this book, you will be able to gain security insights into unstructured big data of any type. The authors of Information Security Analytics bring a wealth of analytics experience to demonstrate practical, hands-on techniques through case studies and using freely-available tools that will allow you to find anomalies and outliers by combining disparate data sets. They also teach you everything you need to know about threat simulation techniques and how to use analytics as a powerful decision-making tool to assess security control and process requirements within your organization. Ultimately, you will learn how to use these simulation techniques to help predict and profile potential risks to your organization. - Written by security practitioners, for security practitioners - Real-world case studies and scenarios are provided for each analytics technique - Learn about open-source analytics and statistical packages, tools, and applications - Step-by-step guidance on how to use analytics tools and how they map to the techniques and scenarios provided - Learn how to design and utilize simulations for what-if scenarios to simulate security events and processes - Learn how to utilize big data techniques to assist in incident response and intrusion analysis |
| data analysis cyber security: Secure Data Science Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan, 2022-04-27 Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science. |
| data analysis cyber security: Cyber Security: Analytics, Technology and Automation Martti Lehto, Pekka Neittaanmäki, 2015-05-30 The book, in addition to the cyber threats and technology, processes cyber security from many sides as a social phenomenon and how the implementation of the cyber security strategy is carried out. The book gives a profound idea of the most spoken phenomenon of this time. The book is suitable for a wide-ranging audience from graduate to professionals/practitioners and researchers. Relevant disciplines for the book are Telecommunications / Network security, Applied mathematics / Data analysis, Mobile systems / Security, Engineering / Security of critical infrastructure and Military science / Security. |
| data analysis cyber security: Cyber Security and Global Information Assurance: Threat Analysis and Response Solutions Knapp, Kenneth J., 2009-04-30 This book provides a valuable resource by addressing the most pressing issues facing cyber-security from both a national and global perspective--Provided by publisher. |
| data analysis cyber security: Cybersecurity and Applied Mathematics Leigh Metcalf, William Casey, 2016-06-07 Cybersecurity and Applied Mathematics explores the mathematical concepts necessary for effective cybersecurity research and practice, taking an applied approach for practitioners and students entering the field. This book covers methods of statistical exploratory data analysis and visualization as a type of model for driving decisions, also discussing key topics, such as graph theory, topological complexes, and persistent homology. Defending the Internet is a complex effort, but applying the right techniques from mathematics can make this task more manageable. This book is essential reading for creating useful and replicable methods for analyzing data. - Describes mathematical tools for solving cybersecurity problems, enabling analysts to pick the most optimal tool for the task at hand - Contains numerous cybersecurity examples and exercises using real world data - Written by mathematicians and statisticians with hands-on practitioner experience |
| data analysis cyber security: Research Anthology on Privatizing and Securing Data Management Association, Information Resources, 2021-04-23 With the immense amount of data that is now available online, security concerns have been an issue from the start, and have grown as new technologies are increasingly integrated in data collection, storage, and transmission. Online cyber threats, cyber terrorism, hacking, and other cybercrimes have begun to take advantage of this information that can be easily accessed if not properly handled. New privacy and security measures have been developed to address this cause for concern and have become an essential area of research within the past few years and into the foreseeable future. The ways in which data is secured and privatized should be discussed in terms of the technologies being used, the methods and models for security that have been developed, and the ways in which risks can be detected, analyzed, and mitigated. The Research Anthology on Privatizing and Securing Data reveals the latest tools and technologies for privatizing and securing data across different technologies and industries. It takes a deeper dive into both risk detection and mitigation, including an analysis of cybercrimes and cyber threats, along with a sharper focus on the technologies and methods being actively implemented and utilized to secure data online. Highlighted topics include information governance and privacy, cybersecurity, data protection, challenges in big data, security threats, and more. This book is essential for data analysts, cybersecurity professionals, data scientists, security analysts, IT specialists, practitioners, researchers, academicians, and students interested in the latest trends and technologies for privatizing and securing data. |
| data analysis cyber security: Deep Learning Applications for Cyber Security Mamoun Alazab, MingJian Tang, 2019-08-14 Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points. |
| data analysis cyber security: Data Warehousing and Data Mining Techniques for Cyber Security Anoop Singhal, 2007-04-06 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. |
| data analysis cyber security: Confluence of AI, Machine, and Deep Learning in Cyber Forensics Misra, Sanjay, Arumugam, Chamundeswari, Jaganathan, Suresh, S., Saraswathi, 2020-12-18 Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication. |
| data analysis cyber security: Dynamic Networks And Cyber-security Niall M Adams, Nicholas A Heard, 2016-03-22 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. |
| data analysis cyber security: Smart Log Data Analytics Florian Skopik, Markus Wurzenberger, Max Landauer, 2021-08-28 This book provides insights into smart ways of computer log data analysis, with the goal of spotting adversarial actions. It is organized into 3 major parts with a total of 8 chapters that include a detailed view on existing solutions, as well as novel techniques that go far beyond state of the art. The first part of this book motivates the entire topic and highlights major challenges, trends and design criteria for log data analysis approaches, and further surveys and compares the state of the art. The second part of this book introduces concepts that apply character-based, rather than token-based, approaches and thus work on a more fine-grained level. Furthermore, these solutions were designed for “online use”, not only forensic analysis, but also process new log lines as they arrive in an efficient single pass manner. An advanced method for time series analysis aims at detecting changes in the overall behavior profile of an observed system and spotting trends and periodicities through log analysis. The third part of this book introduces the design of the AMiner, which is an advanced open source component for log data anomaly mining. The AMiner comes with several detectors to spot new events, new parameters, new correlations, new values and unknown value combinations and can run as stand-alone solution or as sensor with connection to a SIEM solution. More advanced detectors help to determines the characteristics of variable parts of log lines, specifically the properties of numerical and categorical fields. Detailed examples throughout this book allow the reader to better understand and apply the introduced techniques with open source software. Step-by-step instructions help to get familiar with the concepts and to better comprehend their inner mechanisms. A log test data set is available as free download and enables the reader to get the system up and running in no time. This book is designed for researchers working in the field of cyber security, and specifically system monitoring, anomaly detection and intrusion detection. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems. Forward-thinking practitioners, who would benefit from becoming familiar with the advanced anomaly detection methods, will also be interested in this book. |
| data analysis cyber security: Data Analytics and Decision Support for Cybersecurity Iván Palomares Carrascosa, Harsha Kumara Kalutarage, Yan Huang, 2017-08-01 The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic. |
| data analysis cyber security: Security Data Visualization Greg Conti, 2007 An introduction to a range of cyber security issues explains how to utilize graphical approaches to displaying and understanding computer security data, such as network traffic, server logs, and executable files, offering guidelines for identifying a network attack, how to assess a system for vulnerabilities with Afterglow and RUMINT visualization software, and how to protect a system from additional attacks. Original. (Intermediate) |
| data analysis cyber security: Research Anthology on Artificial Intelligence Applications in Security Management Association, Information Resources, 2020-11-27 As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research. |
| data analysis cyber security: Big Data Analytics in Cybersecurity Onur Savas, Julia Deng, 2017-09-18 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 analysis cyber security: Handbook of Research on Network Forensics and Analysis Techniques Shrivastava, Gulshan, Kumar, Prabhat, Gupta, B. B., Bala, Suman, Dey, Nilanjan, 2018-04-06 With the rapid advancement in technology, myriad new threats have emerged in online environments. The broad spectrum of these digital risks requires new and innovative methods for protection against cybercrimes. The Handbook of Research on Network Forensics and Analysis Techniques is a current research publication that examines the advancements and growth of forensic research from a relatively obscure tradecraft to an important part of many investigations. Featuring coverage on a broad range of topics including cryptocurrency, hand-based biometrics, and cyberterrorism, this publication is geared toward professionals, computer forensics practitioners, engineers, researchers, and academics seeking relevant research on the development of forensic tools. |
| data analysis cyber security: How to Measure Anything in Cybersecurity Risk Douglas W. Hubbard, Richard Seiersen, 2016-07-25 A ground shaking exposé on the failure of popular cyber risk management methods How to Measure Anything in Cybersecurity Risk exposes the shortcomings of current risk management practices, and offers a series of improvement techniques that help you fill the holes and ramp up security. In his bestselling book How to Measure Anything, author Douglas W. Hubbard opened the business world's eyes to the critical need for better measurement. This book expands upon that premise and draws from The Failure of Risk Management to sound the alarm in the cybersecurity realm. Some of the field's premier risk management approaches actually create more risk than they mitigate, and questionable methods have been duplicated across industries and embedded in the products accepted as gospel. This book sheds light on these blatant risks, and provides alternate techniques that can help improve your current situation. You'll also learn which approaches are too risky to save, and are actually more damaging than a total lack of any security. Dangerous risk management methods abound; there is no industry more critically in need of solutions than cybersecurity. This book provides solutions where they exist, and advises when to change tracks entirely. Discover the shortcomings of cybersecurity's best practices Learn which risk management approaches actually create risk Improve your current practices with practical alterations Learn which methods are beyond saving, and worse than doing nothing Insightful and enlightening, this book will inspire a closer examination of your company's own risk management practices in the context of cybersecurity. The end goal is airtight data protection, so finding cracks in the vault is a positive thing—as long as you get there before the bad guys do. How to Measure Anything in Cybersecurity Risk is your guide to more robust protection through better quantitative processes, approaches, and techniques. |
| data analysis cyber security: The NICE Cyber Security Framework Izzat Alsmadi, 2019-01-24 This textbook is for courses in cyber security education that follow National Initiative for Cybersecurity Education (NICE) KSAs work roles and framework, that adopt the Competency-Based Education (CBE) method. The book follows the CBT (KSA) general framework, meaning each chapter contains three sections, knowledge and questions, and skills/labs for Skills and Abilities. The author makes an explicit balance between knowledge and skills material in information security, giving readers immediate applicable skills. The book is divided into seven parts: Securely Provision; Operate and Maintain; Oversee and Govern; Protect and Defend; Analysis; Operate and Collect; Investigate. All classroom materials (in the book an ancillary) adhere to the NICE framework. Mirrors classes set up by the National Initiative for Cybersecurity Education (NICE) Adopts the Competency-Based Education (CBE) method of teaching, used by universities, corporations, and in government training Includes content and ancillaries that provide skill-based instruction on compliance laws, information security standards, risk response and recovery, and more |
| data analysis cyber security: Cyber Security, Artificial Intelligence, Data Protection & the Law Robert Walters, Marko Novak, 2021-08-24 This book provides a comparison and practical guide of the data protection laws of Canada, China (Hong Kong, Macau, Taiwan), Laos, Philippines, South Korea, United States and Vietnam. The book builds on the first book Data Protection Law. A Comparative Analysis of Asia-Pacific and European Approaches, Robert Walters, Leon Trakman, Bruno Zeller. As the world comes to terms with Artificial Intelligence (AI), which now pervades the daily lives of everyone. For instance, our smart or Iphone, and smart home technology (robots, televisions, fridges and toys) access our personal data at an unprecedented level. Therefore, the security of that data is increasingly more vulnerable and can be compromised. This book examines the interface of cyber security, AI and data protection. It highlights and recommends that regulators and governments need to undertake wider research and law reform to ensure the most vulnerable in the community have their personal data protected adequately, while balancing the future benefits of the digital economy. |
| data analysis cyber security: Data Analytics for Cybersecurity Vandana P. Janeja, 2022-07-21 As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and end users. Analyzing data across different segments of cybersecurity domains, particularly data generated during cyber-attacks, can help us understand threats better, prevent future cyber-attacks, and provide insights into the evolving cyber threat landscape. This book takes a data oriented approach to studying cyber threats, showing in depth how traditional methods such as anomaly detection can be extended using data analytics and also applies data analytics to non-traditional views of cybersecurity, such as multi domain analysis, time series and spatial data analysis, and human-centered cybersecurity. |
| data analysis cyber security: Real-Time and Retrospective Analyses of Cyber Security Bird, David Anthony, 2020-09-04 Society is continually transforming into a digitally powered reality due to the increased dependence of computing technologies. The landscape of cyber threats is constantly evolving because of this, as hackers are finding improved methods of accessing essential data. Analyzing the historical evolution of cyberattacks can assist practitioners in predicting what future threats could be on the horizon. Real-Time and Retrospective Analyses of Cyber Security is a pivotal reference source that provides vital research on studying the development of cybersecurity practices through historical and sociological analyses. While highlighting topics such as zero trust networks, geopolitical analysis, and cyber warfare, this publication explores the evolution of cyber threats, as well as improving security methods and their socio-technological impact. This book is ideally designed for researchers, policymakers, strategists, officials, developers, educators, sociologists, and students seeking current research on the evolution of cybersecurity methods through historical analysis and future trends. |
| data analysis cyber security: Network and System Security Mirosław Kutyłowski, Jun Zhang, Chao Chen, 2020-12-18 This book constitutes the refereed proceedings of the 14th International Conference on Network and System Security, NSS 2020, held in Melbourne, VIC, Australia, in November 2020. The 17 full and 9 short papers were carefully reviewed and selected from 60 submissions. The selected papers are devoted to topics such as secure operating system architectures, applications programming and security testing, intrusion and attack detection, cybersecurity intelligence, access control, cryptographic techniques, cryptocurrencies, ransomware, anonymity, trust, recommendation systems, as well machine learning problems. Due to the Corona pandemic the event was held virtually. |
| data analysis cyber security: Research Anthology on Big Data Analytics, Architectures, and Applications Information Resources Management Association, 2022 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| data analysis cyber security: Cyber Strategy Carol A. Siegel, Mark Sweeney, 2020-03-23 Cyber Strategy: Risk-Driven Security and Resiliency provides a process and roadmap for any company to develop its unified Cybersecurity and Cyber Resiliency strategies. It demonstrates a methodology for companies to combine their disassociated efforts into one corporate plan with buy-in from senior management that will efficiently utilize resources, target high risk threats, and evaluate risk assessment methodologies and the efficacy of resultant risk mitigations. The book discusses all the steps required from conception of the plan from preplanning (mission/vision, principles, strategic objectives, new initiatives derivation), project management directives, cyber threat and vulnerability analysis, cyber risk and controls assessment to reporting and measurement techniques for plan success and overall strategic plan performance. In addition, a methodology is presented to aid in new initiative selection for the following year by identifying all relevant inputs. Tools utilized include: Key Risk Indicators (KRI) and Key Performance Indicators (KPI) National Institute of Standards and Technology (NIST) Cyber Security Framework (CSF) Target State Maturity interval mapping per initiative Comparisons of current and target state business goals and critical success factors A quantitative NIST-based risk assessment of initiative technology components Responsible, Accountable, Consulted, Informed (RACI) diagrams for Cyber Steering Committee tasks and Governance Boards’ approval processes Swimlanes, timelines, data flow diagrams (inputs, resources, outputs), progress report templates, and Gantt charts for project management The last chapter provides downloadable checklists, tables, data flow diagrams, figures, and assessment tools to help develop your company’s cybersecurity and cyber resiliency strategic plan. |
| data analysis cyber security: Machine Learning and Security Clarence Chio, David Freeman, 2018-01-26 Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions |
| data analysis cyber security: A Data-Driven Computer Security Defense Roger Grimes, 2017-09-26 Most companies are using inefficient computer security defenses which allow hackers to break in at will. It's so bad that most companies have to assume that it is already or can easily be breached. It doesn't have to be this way! A data-driven computer security defense will help any entity better focus on the right threats and defenses. It will create an environment which will help you recognize emerging threats sooner, communicate those threats faster, and defend far more efficiently. What is taught in this book...better aligning defenses to the very threats they are supposed to defend against, will seem commonsense after you read them, but for reasons explained in the book, aren't applied by most companies. The lessons learned come from a 30-year computer security veteran who consulted with hundreds of companies, large and small, who figured out what did and didn't work when defending against hackers and malware. Roger A. Grimes is the author of nine previous books and over 1000 national magazine articles on computer security. Reading A Data-Driven Computer Security Defense will change the way you look at and use computer security for now on. |
| data analysis cyber security: Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities Sanjay Misra, Amit Kumar Tyagi, 2021-05-31 This book provides stepwise discussion, exhaustive literature review, detailed analysis and discussion, rigorous experimentation results (using several analytics tools), and an application-oriented approach that can be demonstrated with respect to data analytics using artificial intelligence to make systems stronger (i.e., impossible to breach). We can see many serious cyber breaches on Government databases or public profiles at online social networking in the recent decade. Today artificial intelligence or machine learning is redefining every aspect of cyber security. From improving organizations’ ability to anticipate and thwart breaches, protecting the proliferating number of threat surfaces with Zero Trust Security frameworks to making passwords obsolete, AI and machine learning are essential to securing the perimeters of any business. The book is useful for researchers, academics, industry players, data engineers, data scientists, governmental organizations, and non-governmental organizations. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
