Data Driven Cybersecurity
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Data Driven Cybersecurity
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Author : Mariano Mattei
language : en
Publisher: Simon and Schuster
Release Date : 2025-08-26
Data Driven Cybersecurity written by Mariano Mattei and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-26 with Computers categories.
Measure, improve, and communicate the value of your security program. Every business decision should be driven by data—and cyber security is no exception. In Data-Driven Cybersecurity, you'll master the art and science of quantifiable cybersecurity, learning to harness data for enhanced threat detection, response, and mitigation. You’ll turn raw data into meaningful intelligence, better evaluate the performance of your security teams, and proactively address the vulnerabilities revealed by the numbers. Data-Driven Cybersecurity will teach you how to: • Align a metrics program with organizational goals • Design real-time threat detection dashboards • Predictive cybersecurity using AI and machine learning • Data-driven incident response • Apply the ATLAS methodology to reduce alert fatigue • Create compelling metric visualizations Data-Driven Cybersecurity teaches you to implement effective, data-driven cybersecurity practices—including utilizing AI and machine learning for detection and prediction. Throughout, the book presents security as a core part of organizational strategy, helping you align cyber security with broader business objectives. If you’re a CISO or security manager, you’ll find the methods for communicating metrics to non-technical stakeholders invaluable. Foreword by Joseph Steinberg. About the technology A data-focused approach to cybersecurity uses metrics, analytics, and automation to detect threats earlier, respond faster, and align security with business goals. About the book Data-Driven Cybersecurity shows you how to turn complex security metrics into evidence-based security practices. You’ll learn to define meaningful KPIs, communicate risk to stakeholders, and turn complex data into clear action. You’ll begin by answering the important questions: what makes a “good” security metric? How can I align security with broader business objectives? What makes a robust data-driven security management program? Python scripts and Jupyter notebooks make collecting security data easy and help build a real-time threat detection dashboards. You’ll even see how AI and machine learning can proactively predict cybersecurity incidents! What's inside • Improve your alert system using the ATLAS framework • Elevate your organization’s security posture • Statistical and ML techniques for threat detection • Executive buy-in and strategic investment About the reader For readers familiar with the basics of cybersecurity and data analysis. About the author Mariano Mattei is a professor at Temple University and an information security professional with over 30 years of experience in cybersecurity and AI innovation. Table of Contents Part 1 Building the foundation 1 Introducing cybersecurity metrics 2 Cybersecurity analytics toolkit 3 Implementing a security metrics program 4 Integrating metrics into business strategy Part 2 The metrics that matter 5 Establishing the foundation 6 Foundations of cyber risk 7 Protecting your assets 8 Continuous threat detection 9 Incident management and recovery Part 3 Beyond the basics: Advanced analytics, machine learning and AI 10 Advanced cybersecurity metrics 11 Advanced statistical analysis 12 Advanced machine learning analysis 13 Generative AI in cybersecurity metrics
Advances In Malware And Data Driven Network Security
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Author : Gupta, Brij B.
language : en
Publisher: IGI Global
Release Date : 2021-11-12
Advances In Malware And Data Driven Network Security written by Gupta, Brij B. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-12 with Computers categories.
Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware – to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.
Safe Data Driven Control For Cyber Physical Systems
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Author : Arbi, Adnène
language : en
Publisher: IGI Global
Release Date : 2025-09-30
Safe Data Driven Control For Cyber Physical Systems written by Arbi, Adnène and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-30 with Computers categories.
Cyber-physical systems, which integrate computation, networking, and physical processes, apply to various sectors like autonomous vehicles and smart grids. As these systems become more complex and interconnected, ensuring their safety while achieving high performance emerges as a critical challenge. Traditional model-based control methods often struggle to cope with uncertainties, unmodeled dynamics, and evolving environments. Alternatively, data-driven control leverages real-time data to adapt and optimize control strategies. The use of data-driven methods raises safety concerns, leading to growing research focused on safe data driven control. Safe Data-Driven Control for Cyber-Physical Systems explores the integration of information and communication technologies (ICT) in control-command systems. It examines how this convergence leads to the emergence of new cyber-physical systems for enhanced control, safety, and security. This book covers topics such as threat management, data privacy, and machine learning, and is a useful resource for business owners, computer engineers, security professionals, academicians, researchers, and data scientists.
Data Driven Cybersecurity Defense
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Author : K. Anitha Kumari
language : en
Publisher: Wiley-Scrivener
Release Date : 2026-04-14
Data Driven Cybersecurity Defense written by K. Anitha Kumari and has been published by Wiley-Scrivener this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-04-14 with Computers categories.
Ai Driven Cybersecurity And Threat Intelligence
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Author : Iqbal H. Sarker
language : en
Publisher: Springer Nature
Release Date : 2024-04-28
Ai Driven Cybersecurity And Threat Intelligence written by Iqbal H. Sarker and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-28 with Computers categories.
This book explores the dynamics of how AI (Artificial Intelligence) technology intersects with cybersecurity challenges and threat intelligence as they evolve. Integrating AI into cybersecurity not only offers enhanced defense mechanisms, but this book introduces a paradigm shift illustrating how one conceptualize, detect and mitigate cyber threats. An in-depth exploration of AI-driven solutions is presented, including machine learning algorithms, data science modeling, generative AI modeling, threat intelligence frameworks and Explainable AI (XAI) models. As a roadmap or comprehensive guide to leveraging AI/XAI to defend digital ecosystems against evolving cyber threats, this book provides insights, modeling, real-world applications and research issues. Throughout this journey, the authors discover innovation, challenges, and opportunities. It provides a holistic perspective on the transformative role of AI in securing the digital world. Overall, the useof AI can transform the way one detects, responds and defends against threats, by enabling proactive threat detection, rapid response and adaptive defense mechanisms. AI-driven cybersecurity systems excel at analyzing vast datasets rapidly, identifying patterns that indicate malicious activities, detecting threats in real time as well as conducting predictive analytics for proactive solution. Moreover, AI enhances the ability to detect anomalies, predict potential threats, and respond swiftly, preventing risks from escalated. As cyber threats become increasingly diverse and relentless, incorporating AI/XAI into cybersecurity is not just a choice, but a necessity for improving resilience and staying ahead of ever-changing threats. This book targets advanced-level students in computer science as a secondary textbook. Researchers and industry professionals working in various areas, such as Cyber AI, Explainable and Responsible AI, Human-AI Collaboration, Automation and Intelligent Systems, Adaptive and Robust Security Systems, Cybersecurity Data Science and Data-Driven Decision Making will also find this book useful as reference book.
Intelligent Data Driven Techniques For Security Of Digital Assets
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Author : Arun Kumar Rana
language : en
Publisher: CRC Press
Release Date : 2025-03-24
Intelligent Data Driven Techniques For Security Of Digital Assets written by Arun Kumar Rana and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-24 with Technology & Engineering categories.
The book covers the role of emerging technologies such as blockchain technology, machine learning, IoT, cryptography, etc., in digital asset management. It further discusses digital asset management applications in different domains such as healthcare, travel industry, image processing, and our daily life activities to maintain privacy and confidentiality. This book: • Discusses techniques for securing and protecting digital assets in collaborative environments, where multiple organizations need access to the same resources. • Explores how artificial intelligence can be used to automate the management of digital assets, and how it can be used to improve security and privacy. • Explains the role of emerging technology such as blockchain technology for transforming conventional business models. • Highlights the importance of machine learning techniques in maintaining the privacy and security of data. • Covers encryption and decryption techniques, their advantages and role in improving the privacy of data. The text is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, information technology, and business management.
Cybersecurity In Intelligent Networking Systems
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Author : Shengjie Xu
language : en
Publisher: John Wiley & Sons
Release Date : 2022-11-02
Cybersecurity In Intelligent Networking Systems written by Shengjie Xu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-02 with Computers categories.
CYBERSECURITY IN INTELLIGENT NETWORKING SYSTEMS Help protect your network system with this important reference work on cybersecurity Cybersecurity and privacy are critical to modern network systems. As various malicious threats have been launched that target critical online services—such as e-commerce, e-health, social networks, and other major cyber applications—it has become more critical to protect important information from being accessed. Data-driven network intelligence is a crucial development in protecting the security of modern network systems and ensuring information privacy. Cybersecurity in Intelligent Networking Systems provides a background introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It offers a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing on encryption-based security protocol, this book also highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology. Cybersecurity in Intelligent Networking Systems readers will also find: Fundamentals in AI for cybersecurity, including artificial intelligence, machine learning, and security threats Latest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryption Key areas in adversarial machine learning, from both offense and defense perspectives Descriptions of network anomalies and cyber threats Background information on data-driven network intelligence for cybersecurity Robust and secure edge intelligence for network anomaly detection against cyber intrusions Detailed descriptions of the design of privacy-preserving security protocols Cybersecurity in Intelligent Networking Systems is an essential reference for all professional computer engineers and researchers in cybersecurity and artificial intelligence, as well as graduate students in these fields.
Enhancing Cybersecurity With Machine Learning A Data Driven Approach To Detect And Mitigate Threats
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Author : Navjot Singh Talwandi
language : en
Publisher: IIP Iterative International Publishers
Release Date : 2025-10-10
Enhancing Cybersecurity With Machine Learning A Data Driven Approach To Detect And Mitigate Threats written by Navjot Singh Talwandi and has been published by IIP Iterative International Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-10 with Computers categories.
The ever-evolving cyber landscape presents an ongoing challenge to traditional security measures and threat detection methodologies. As cyber threats grow in complexity, conventional defences are often rendered insufficient in identifying and mitigating sophisticated attacks. The book before you, Enhancing Cybersecurity with Machine Learning: A Data-Driven Approach to Detect and Mitigate Threats, delves into the transformative role of machine learning (ML) and artificial intelligence (AI) in modern cybersecurity, offering cutting-edge insights into data-driven security strategies. This volume provides a comprehensive exploration of ML-driven techniques, demonstrating how they enhance cybersecurity by automating threat detection, analysing vast datasets, and responding to anomalies with greater accuracy. Covering a diverse range of topics, from intrusion detection systems and malware analysis to behavioural analytics and adversarial machine learning, the chapters present a balanced mix of theoretical foundations and practical applications. Our goal is to equip readers with a deep understanding of the intersection between cybersecurity and machine learning, enabling them to harness AI-driven security solutions effectively. Through the contributions of seasoned experts and researchers, this book highlights real-world implementations, emerging trends, and challenges, paving the way for future advancements in cybersecurity. This collection serves as a valuable resource for researchers, cybersecurity professionals, data scientists, and students seeking to explore data-driven security solutions. Whether you are an industry practitioner or an academic enthusiast, we hope this book provides an indispensable guide to navigating the complexities of cybersecurity in the age of machine learning.
Practical Threat Intelligence And Data Driven Threat Hunting
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Author : Valentina Costa-Gazcón
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-12
Practical Threat Intelligence And Data Driven Threat Hunting written by Valentina Costa-Gazcón and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-12 with Computers categories.
Get to grips with cyber threat intelligence and data-driven threat hunting while exploring expert tips and techniques Key Features Set up an environment to centralize all data in an Elasticsearch, Logstash, and Kibana (ELK) server that enables threat hunting Carry out atomic hunts to start the threat hunting process and understand the environment Perform advanced hunting using MITRE ATT&CK Evals emulations and Mordor datasets Book DescriptionThreat hunting (TH) provides cybersecurity analysts and enterprises with the opportunity to proactively defend themselves by getting ahead of threats before they can cause major damage to their business. This book is not only an introduction for those who don’t know much about the cyber threat intelligence (CTI) and TH world, but also a guide for those with more advanced knowledge of other cybersecurity fields who are looking to implement a TH program from scratch. You will start by exploring what threat intelligence is and how it can be used to detect and prevent cyber threats. As you progress, you’ll learn how to collect data, along with understanding it by developing data models. The book will also show you how to set up an environment for TH using open source tools. Later, you will focus on how to plan a hunt with practical examples, before going on to explore the MITRE ATT&CK framework. By the end of this book, you’ll have the skills you need to be able to carry out effective hunts in your own environment.What you will learn Understand what CTI is, its key concepts, and how it is useful for preventing threats and protecting your organization Explore the different stages of the TH process Model the data collected and understand how to document the findings Simulate threat actor activity in a lab environment Use the information collected to detect breaches and validate the results of your queries Use documentation and strategies to communicate processes to senior management and the wider business Who this book is for If you are looking to start out in the cyber intelligence and threat hunting domains and want to know more about how to implement a threat hunting division with open-source tools, then this cyber threat intelligence book is for you.
Data Driven Mining Learning And Analytics For Secured Smart Cities
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Author : Chinmay Chakraborty
language : en
Publisher: Springer Nature
Release Date : 2021-04-28
Data Driven Mining Learning And Analytics For Secured Smart Cities written by Chinmay Chakraborty and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Computers categories.
This book provides information on data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart cities. This book aims to attract works on multidisciplinary research spanning across the computer science and engineering, environmental studies, services, urban planning and development, social sciences and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative solutions and big data-powered applications to cope with the real world challenges for building smart cities.