Download or read book Cybersecurity Data Science written by Scott Mongeau and published by Springer Nature. This book was released on 2021-10-01 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Cybersecurity Data Science written by Scott Mongeau and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Science For Cyber security written by Adams Niall M and published by World Scientific. This book was released on 2018-09-25 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Big Data Analytics in Cybersecurity written by Onur Savas and published by CRC Press. This book was released on 2017-09-18 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Cybersecurity Analytics written by Rakesh M. Verma and published by CRC Press. This book was released on 2019-11-27 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Mining and Machine Learning in Cybersecurity written by Sumeet Dua and published by CRC Press. This book was released on 2016-04-19 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible

Download or read book Machine Learning for Cybersecurity Cookbook written by Emmanuel Tsukerman and published by Packt Publishing Ltd. This book was released on 2019-11-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key FeaturesManage data of varying complexity to protect your system using the Python ecosystemApply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineeringAutomate your daily workflow by addressing various security challenges using the recipes covered in the bookBook Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learnLearn how to build malware classifiers to detect suspicious activitiesApply ML to generate custom malware to pentest your securityUse ML algorithms with complex datasets to implement cybersecurity conceptsCreate neural networks to identify fake videos and imagesSecure your organization from one of the most popular threats – insider threatsDefend against zero-day threats by constructing an anomaly detection systemDetect web vulnerabilities effectively by combining Metasploit and MLUnderstand how to train a model without exposing the training dataWho this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.

Download or read book Intelligent Computing and Innovation on Data Science written by Sheng-Lung Peng and published by Springer Nature. This book was released on 2021-09-27 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers high-quality papers presented at 2nd International Conference on Technology Innovation and Data Sciences (ICTIDS 2021), organized by Lincoln University, Malaysia from 19 – 20 February 2021. It covers wide range of recent technologies like artificial intelligence and machine learning, big data and data sciences, Internet of Things (IoT), and IoT-based digital ecosystem. The book brings together works from researchers, scientists, engineers, scholars and students in the areas of engineering and technology, and provides an opportunity for the dissemination of original research results, new ideas, research and development, practical experiments, which concentrate on both theory and practices, for the benefit of common man.

Download or read book New Solutions for Cybersecurity written by Howard Shrobe and published by MIT Press. This book was released on 2018-01-26 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experts from MIT explore recent advances in cybersecurity, bringing together management, technical, and sociological perspectives. Ongoing cyberattacks, hacks, data breaches, and privacy concerns demonstrate vividly the inadequacy of existing methods of cybersecurity and the need to develop new and better ones. This book brings together experts from across MIT to explore recent advances in cybersecurity from management, technical, and sociological perspectives. Leading researchers from MIT's Computer Science & Artificial Intelligence Lab, the MIT Media Lab, MIT Sloan School of Management, and MIT Lincoln Lab, along with their counterparts at Draper Lab, the University of Cambridge, and SRI, discuss such varied topics as a systems perspective on managing risk, the development of inherently secure hardware, and the Dark Web. The contributors suggest approaches that range from the market-driven to the theoretical, describe problems that arise in a decentralized, IoT world, and reimagine what optimal systems architecture and effective management might look like. Contributors YNadav Aharon, Yaniv Altshuler, Manuel Cebrian, Nazli Choucri, André DeHon, Ryan Ellis, Yuval Elovici, Harry Halpin, Thomas Hardjono, James Houghton, Keman Huang, Mohammad S. Jalali, Priscilla Koepke, Yang Lee, Stuart Madnick, Simon W. Moore, Katie Moussouris, Peter G. Neumann, Hamed Okhravi, Jothy Rosenberg, Hamid Salim,Michael Siegel, Diane Strong, Gregory T. Sullivan, Richard Wang, Robert N. M. Watson, Guy Zyskind An MIT Connection Science and Engineering Book

Download or read book Essential Cybersecurity Science written by Josiah Dykstra and published by "O'Reilly Media, Inc.". This book was released on 2015-12-08 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re involved in cybersecurity as a software developer, forensic investigator, or network administrator, this practical guide shows you how to apply the scientific method when assessing techniques for protecting your information systems. You’ll learn how to conduct scientific experiments on everyday tools and procedures, whether you’re evaluating corporate security systems, testing your own security product, or looking for bugs in a mobile game. Once author Josiah Dykstra gets you up to speed on the scientific method, he helps you focus on standalone, domain-specific topics, such as cryptography, malware analysis, and system security engineering. The latter chapters include practical case studies that demonstrate how to use available tools to conduct domain-specific scientific experiments. Learn the steps necessary to conduct scientific experiments in cybersecurity Explore fuzzing to test how your software handles various inputs Measure the performance of the Snort intrusion detection system Locate malicious “needles in a haystack” in your network and IT environment Evaluate cryptography design and application in IoT products Conduct an experiment to identify relationships between similar malware binaries Understand system-level security requirements for enterprise networks and web services

Download or read book Malware Data Science written by Joshua Saxe and published by No Starch Press. This book was released on 2018-09-25 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hands On Machine Learning for Cybersecurity written by Soma Halder and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get into the world of smart data security using machine learning algorithms and Python libraries Key FeaturesLearn machine learning algorithms and cybersecurity fundamentalsAutomate your daily workflow by applying use cases to many facets of securityImplement smart machine learning solutions to detect various cybersecurity problemsBook 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 learnUse machine learning algorithms with complex datasets to implement cybersecurity conceptsImplement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problemsLearn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDAUnderstand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimesUse TensorFlow in the cybersecurity domain and implement real-world examplesLearn how machine learning and Python can be used in complex cyber issuesWho 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

Download or read book Cybersecurity Data Science Projects for Students written by Dr Emmanuel Tsukerman and published by . This book was released on 2020-08-17 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Looking to enter the profession but don't know where to start? Projects for Students will help you on the path to ultimately becoming a badass hacker and security expert who knows how to use machine learning to break and secure systems. In this one-of-its-kind workbook, you will be guided on interesting and fun projects that will allow you to display your skills and growing knowledge. The projects are purposefully designed to be at the perfect balance of challenge (i.e., a beginner can complete them with a bit of patience) and interest so that solving them is sure to impress hiring managers, employers and co-workers. The course uses python and tensorflow for deep learning. It is hands on and each project is immersive several-week experience. Students expected to get their hands dirty with malware, neural networks and DeepFakes!✔ Classify and Detect Malware.✔ Catch Network Intruders.✔ Detect Insider Threats.✔ Break CAPTCHAs.✔ Construct an Evolutionary Fuzzer.✔ Construct Adversarial Attacks on Deep Neural Networks.✔ Impersonate Voice.✔ Create DeepFakes.✔ Generate Fake Reviews.

Download or read book Data Analysis for Network Cyber Security written by Niall Adams and published by World Scientific. This book was released on 2014-02-28 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Machine Learning Approaches in Cyber Security Analytics written by Tony Thomas and published by Springer Nature. This book was released on 2019-12-16 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Network Security Through Data Analysis written by Michael S Collins and published by "O'Reilly Media, Inc.". This book was released on 2014-02-10 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Machine Learning and Security written by Clarence Chio and published by "O'Reilly Media, Inc.". This book was released on 2018-01-26 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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