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2024 | Book

Introduction to AI-Driven Cybersecurity and Threat Intelligence Read chapter Read first chapter

AI-Driven Cybersecurity and Threat Intelligence

Cyber Automation, Intelligent Decision-Making and Explainability

Author: Iqbal H. Sarker

Publisher: Springer Nature Switzerland

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

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.

Table of Contents

Frontmatter

Part I

Frontmatter
Chapter 1. Introduction to AI-Driven Cybersecurity and Threat Intelligence
Abstract
With the convergence of artificial intelligence (AI) and cybersecurity, a new paradigm has emerged in how we defend against evolving digital threats. This book explores the dynamic landscape of AI-driven cybersecurity and threat intelligence, emphasizing how the computing and analytical power and decision-making capabilities of AI technologies are revolutionizing the detection, prevention, and response to cyberattacks. AI and machine learning algorithms can analyze vast datasets quickly, identify patterns, and predict potential threats, enabling organizations to strengthen their digital infrastructure proactively. In this book, we have bestowed a comprehensive study on this topic that explores not only the potentiality of cyber threat intelligence but also how different AI methods such as machine learning modeling, deep learning modeling, data science process, generative AI modeling, natural language processing with large language modeling, etc. can be employed to provide intelligent cybersecurity services. We have also discussed various essential real-world application areas such as Internet of Things and smart cities, industrial control systems and operational technology environments, critical infrastructures, cyber-physical systems, digital twins, and relevant others where AI-driven cybersecurity and threat intelligence could be useful for effective and automated solutions. Throughout this book, we have also highlighted relevant research issues and challenges as well as their potential solution directions within the context of AI-based cybersecurity and threat intelligence.
Iqbal H. Sarker
Chapter 2. Cybersecurity Background Knowledge: Terminologies, Attack Frameworks, and Security Life Cycle
Abstract
This chapter provides a foundational understanding of cybersecurity concepts, including terminologies and attack frameworks like the cyber kill chain and MITRE ATT&CK, as well as the cybersecurity life cycle. In this chapter, key terms regarding threats, vulnerabilities, security controls, and relevant emerging technologies associated with AI are clarified, enabling effective communication within the cybersecurity field. Examining attack frameworks, which encompass the sequential stages of the cyber kill chain and the tactical matrix of MITRE ATT&CK, provides valuable insight into adversary tactics. Furthermore, the cybersecurity life cycle emphasizes a systematic approach to cybersecurity management, emphasizing risk assessment, continuous monitoring, and adaptive security measures. The purpose of this chapter is to provide readers with the knowledge and understanding necessary to navigate the complex landscape of cybersecurity with a strategic and informed perspective, providing a solid foundation for further exploration.
Iqbal H. Sarker

Part II

Frontmatter
Chapter 3. Learning Technologies: Toward Machine Learning and Deep Learning for Cybersecurity
Abstract
This chapter explores the transformative landscape of learning technologies, focusing specifically on machine learning and deep learning techniques used in cybersecurity. As digital threats become increasingly sophisticated and complex, conventional cybersecurity approaches are becoming inadequate. The chapter explores how machine learning and deep learning algorithms can enhance threat detection, anomaly analysis, and overall security posture. Using key concepts and methodologies, the chapter describes how advanced technologies can be used to strengthen cyber defenses, providing insights into the challenges, opportunities, and future prospects of machine learning and deep learning-based cybersecurity modeling. The overall goal is not only to explore the state of machine learning and relevant methodologies but also to highlight their potential for enhancing cybersecurity in the future. This chapter thus contributes to the ongoing discussion about how to strengthen digital landscapes against an ever-evolving cyber threat landscape by exploring cutting-edge advancements in learning technologies.
Iqbal H. Sarker
Chapter 4. Detecting Anomalies and Multi-attacks Through Cyber Learning: An Experimental Analysis
Abstract
Detecting cyber-anomalies and attacks are becoming a rising concern these days in the domain of cybersecurity. The knowledge of artificial intelligence (AI), particularly the machine learning techniques, can be used to tackle these issues. However, the effectiveness of a learning-based security model may vary depending on the security features and the data characteristics. In this chapter, we present a machine learning-based cybersecurity modeling with correlated-feature selection and a comprehensive empirical analysis on the effectiveness of various machine learning-based security models. In our cyber learning modeling, we take into account a binary classification model for detecting anomalies and multi-class classification model for various types of cyberattacks. To build the security model, we first employ the popular ten machine learning classification techniques, such as naive Bayes, logistic regression, stochastic gradient descent, K-nearest neighbors, support vector machine, decision tree, random forest, adaptive boosting, extreme gradient boosting, as well as linear discriminant analysis. We then present the artificial neural network-based security model considering multiple hidden layers. The effectiveness of these learning-based security models is examined by conducting a range of experiments utilizing the two most popular security datasets, UNSW-NB15 and NSL-KDD. Overall, this chapter aims to serve as a reference point for data-driven security modeling through our experimental analysis and findings in the context of cybersecurity.
Iqbal H. Sarker
Chapter 5. Generative AI and Large Language Modeling in Cybersecurity
Abstract
Cybersecurity is encountering new challenges demanding innovative solutions due to the complexity and frequency of cyberattacks progressing. Artificial intelligence (AI), particularly generative AI, has emerged as a promising technology with the potential to revolutionize current cybersecurity modeling and practices. This chapter provides a comprehensive overview of generative AI and large language modeling (LLM) in the context of cybersecurity, highlighting its potential benefits, challenges, and diverse methods. A variety of machine and deep learning techniques including generative adversarial networks (GANs), variational autoencoders (VAEs), and deep neural networks that can mimic and generate data are included. In the realm of cybersecurity, generative AI plays a multifaceted role including the development of realistic honeypots, deceiving adversaries, producing simulated threat data for security system training, and enhancing anomaly detection capabilities. We also explore cybersecurity large language modeling, i.e., “CyberLLM” and discuss multi-stages of our suggested LLM-based framework highlighting its potential to solve diverse cybersecurity issues. This chapter further explores the challenges and opportunities for generative AI emphasizing the potential for enhanced threat mitigation and resilience in a constantly evolving cyber threat environment.
Iqbal H. Sarker
Chapter 6. Cybersecurity Data Science: Toward Advanced Analytics, Knowledge, and Rule Discovery for Explainable AI Modeling
Abstract
In a computing context, cybersecurity technology and operations are constantly changing, and data science is driving the change. Building a data-driven model that extracts patterns in cybersecurity incidents is the key to automating and intelligently managing a security system. This chapter mainly explores the convergence of cybersecurity and data science exploring its transformative potential in fortifying digital defenses. Throughout the chapter, advanced analytics, knowledge, and rule discovery as well as corresponding data-driven framework are highlighted within the broader area of cybersecurity data science. An emphasis is given to the pivotal role of explainable modeling in comprehending and mitigating sophisticated cyber threats as the threat landscape evolves. Thus the role of knowledge and rule discovery is explored briefly advocating for a paradigm shift toward explainable modeling to address the evolving nature of today’s diverse cyber threats. Data-driven insights and knowledge discovery are explored through methodologies, tools, and best practices, providing a roadmap for practitioners and researchers. Overall, this chapter describes data-driven real-world applications in the context of cybersecurity that not only empower organizations to be proactive in their cyber defense but also highlight the need for transparency and explainable modeling.
Iqbal H. Sarker

Part III

Frontmatter
Chapter 7. AI-Enabled Cybersecurity for IoT and Smart City Applications
Abstract
AI-driven cybersecurity is crucial to enhancing the resilience of the Internet of Things (IoT) and smart city ecosystems. Due to the dynamic and heterogeneous nature of IoT devices, these interconnected networks have become an integral part of urban infrastructure. Using artificial intelligence, particularly machine learning algorithms, enables proactive threat detection, anomaly identification, and rapid response to emerging cyber risks. The AI models can adapt to evolving attack vectors, analyze the massive streams of data generated by the Internet of Things (IoT), and distinguish normal patterns from potential security breaches. The transformative approach not only mitigates known threats but also uncovers new vulnerabilities in smart city applications. Overall, AI-driven cybersecurity protects IoT and smart city infrastructures against sophisticated cyber threats by continuously learning and evolving, thereby fostering a secure and resilient urban digital landscape.
Iqbal H. Sarker
Chapter 8. AI for Enhancing ICS/OT Cybersecurity
Abstract
In today’s industrial environments, advanced technologies have become increasingly integrated, increasing vulnerabilities and risks related to cyber threats. This chapter explores the transformative role of artificial intelligence (AI) in enhancing the security of industrial control systems (ICS) and operational technology (OT) environments. Increasing connectivity and complexity of industrial networks often make traditional cybersecurity measures ineffective against sophisticated threats. In this chapter, we discuss how AI technologies, including machine learning and behavioral analysis, can be used for detecting anomalies, predicting threats, and responding to incidents in real time. This chapter thus emphasizes AI’s potentiality to enhance the resilience of ICS/OT ecosystems by leveraging AI-driven anomaly detection and adaptive security measures. In addition, it discusses the practical implications, challenges, and lessons learned in implementing AI solutions to safeguard critical infrastructure from evolving cyber risks.
Iqbal H. Sarker
Chapter 9. AI for Critical Infrastructure Protection and Resilience
Abstract
This chapter explores how artificial intelligence (AI) can be used to enhance the protection and resilience of critical infrastructure. Society is becoming increasingly dependent on interconnected systems, which makes critical infrastructure more vulnerable to cyber threats and other risks. In this chapter, AI technologies are strategically integrated to fortify critical infrastructure against potential disruptions. Using machine learning and predictive analytics, it discusses advanced AI algorithms for threat detection, risk assessment, and adaptive response mechanisms. The chapter also discusses how AI can enable real-time monitoring, predictive maintenance, and automated response systems to build resilient infrastructure. A comprehensive review of case studies and emerging technologies provides valuable insights into how AI can be used to safeguard critical infrastructure in the face of dynamic challenges and evolving threats.
Iqbal H. Sarker
Chapter 10. CyberAI: A Comprehensive Summary of AI Variants, Explainable and Responsible AI for Cybersecurity
Abstract
The integration of cybersecurity and artificial intelligence (AI), referred to as “CyberAI,” represents a dynamic and transformative landscape. This chapter outlines the diverse landscape of AI variants, as well as their diverse real-world applications in bolstering cybersecurity. The discourse explores the importance of explainable AI and emphasizes the need for transparent models to increase interpretability and user trust in cybersecurity applications. Moreover, the chapter underlines the significance of responsible AI practices, such as fairness, inclusivity, and accountability, in shaping ethical and sustainable uses of AI in cybersecurity. Through a comprehensive exploration of AI variants in diverse real-world application areas and a focus on the principles of explainability and responsibility, this chapter provides insights that are crucial for navigating the intricate intersection of AI and cybersecurity. Lessons learned from the comprehensive summary contribute to a nuanced understanding for practitioners, researchers, and policymakers, which will enable them to make informed decisions and advance secure digital ecosystems.
Iqbal H. Sarker
Metadata
Title
AI-Driven Cybersecurity and Threat Intelligence
Author
Iqbal H. Sarker
Copyright Year
2024
Electronic ISBN
978-3-031-54497-2
Print ISBN
978-3-031-54496-5
DOI
https://doi.org/10.1007/978-3-031-54497-2

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