國立虎尾科技大學 |

The Symbiosis of Trust and AI: Scientific Foundations for Strategic Network Security, Autonomous Resilience, and Prescriptive Governance /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The Symbiosis of Trust and AI: Scientific Foundations for Strategic Network Security, Autonomous Resilience, and Prescriptive Governance // Yunfei Ge.
Author:	Ge, Yunfei,
Description:	1 electronic resource (294 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31559144
ISBN:	9798384450894

The Symbiosis of Trust and AI: Scientific Foundations for Strategic Network Security, Autonomous Resilience, and Prescriptive Governance /
Ge, Yunfei,

The Symbiosis of Trust and AI: Scientific Foundations for Strategic Network Security, Autonomous Resilience, and Prescriptive Governance /Yunfei Ge. - 1 electronic resource (294 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

The rapid development of network systems, driven by innovations like 5G communications, Industrial 4.0, and Artificial Intelligence (AI)-assisted services, has led to a more complex network environment. However, the highly connected feature also introduces sophisticated threats, making security a significant challenge. Trust, a fundamental element in cyber security, becomes crucial in networked systems as it influences multiple dimensions (e.g., defense policy, identity evaluation, system performance) at different phases (e.g., preparation phase, operation phase, outcome phase). Despite the advancements, existing solutions relying on perimeter-based defense and rule-based trust evaluation fall short in addressing sophisticated threats like Advanced Persistent Threats (APTs), insider threats, identity fraud, etc. This highlights the need for a new approach to understanding trust in networked systems.This dissertation explores the symbiotic relationship between AI and trust within the realm of cybersecurity in networked systems, where AI transforms our analysis and utilization of trust to achieve strategic network security and autonomous cyber resilience. Conversely, the trustworthiness of AI dictates its governance, fostering responsible adaptation and use. By examining their interdependence, this work aims to establish a scientific foundation for next-generation network security, achieving a positive equilibrium where AI and trust mutually reinforce each other. The dissertation begins with an introduction to trust and AI in networked systems security in Chapter 1, followed by an overview of cybersecurity terminologies and game theory in Chapter 2. The main contributions are categorized into three areas: strategic penetration testing for trustworthy networked systems, autonomous zero-trust mechanisms for cyber resilience, and prescriptive governance frameworks for AI-powered networks. The first two parts delve into how AI enhances trust in network security, while the third part discusses how trust can improve AI utilization and adoption.Part I of the dissertation focuses on strategic penetration testing for trustworthy networked systems. In response to potential adversarial threats, penetration testing has emerged as a crucial solution for uncovering system vulnerabilities and assessing network security through authorized ethical attacks. In Chapter 3, we introduce a meta-game-based automated penetration testing framework (MEGA-PT), which incorporates micro tactic games for node-level local interactions and a macro strategy process for network-level attack chains. This dual-level modeling facilitates distributed, adaptive, collaborative, and rapid penetration testing. MEGA-PT establishes fundamental principles to guide future automated penetration testing and offers a strategic testing method to verify and enhance the trustworthiness of networked systems during the preparation stage. Furthermore, we propose the idea of agent-based penetration testing aims to create open source technology that can support the creation of a sustainable competition league in which fresh cybersecurity datasets are continually generated.Cyber attacks are evolving and becoming more targeted, which requires systems to be resilient and capable of autonomous response in the face of cyber incidents. In Part II, we propose several autonomous zero-trust mechanisms for cyber resilience. Chapter 4 presents a game-theoretic zero-trust authentication framework (GAZETA) designed to deter lateral movement penetration and dynamically adapt authentication policies. In Chapter 5, we introduce a scenario-agnostic zero-trust defense (SA-ZTD) for autonomous policy generation across diverse scenarios. Lastly, we propose a task-aware and resilient trust management scheme (E2ETrust) for Industrial Internet of Things (IoT) systems, leveraging a risk-sensitive approach to optimize task performance.After discussing how AI techniques (e.g., learning and game theory) enhance trust-based methods in network security, the third part examines the relationship from the other direction and discusses how trust improves the governance and adaptation of AI-powered systems. In Chapter 6, we investigate effective and ethical responsibility attribution in AI-induced systems, establishing a computational framework (CARE) using reflective equilibrium to guide AI governance policy-making. In Chapter 7, an AI liability insurance framework for medical devices is introduced, analyzing its role in advancing responsible AI adoption in healthcare and exploring the impact of data poisoning attacks and the moral hazard problem within the framework. These prescriptive governance frameworks aim to enhance the trustworthiness and responsible use of AI-powered systems.

English

ISBN: 9798384450894Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Accountability

The Symbiosis of Trust and AI: Scientific Foundations for Strategic Network Security, Autonomous Resilience, and Prescriptive Governance /
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520 $a The rapid development of network systems, driven by innovations like 5G communications, Industrial 4.0, and Artificial Intelligence (AI)-assisted services, has led to a more complex network environment. However, the highly connected feature also introduces sophisticated threats, making security a significant challenge. Trust, a fundamental element in cyber security, becomes crucial in networked systems as it influences multiple dimensions (e.g., defense policy, identity evaluation, system performance) at different phases (e.g., preparation phase, operation phase, outcome phase). Despite the advancements, existing solutions relying on perimeter-based defense and rule-based trust evaluation fall short in addressing sophisticated threats like Advanced Persistent Threats (APTs), insider threats, identity fraud, etc. This highlights the need for a new approach to understanding trust in networked systems.This dissertation explores the symbiotic relationship between AI and trust within the realm of cybersecurity in networked systems, where AI transforms our analysis and utilization of trust to achieve strategic network security and autonomous cyber resilience. Conversely, the trustworthiness of AI dictates its governance, fostering responsible adaptation and use. By examining their interdependence, this work aims to establish a scientific foundation for next-generation network security, achieving a positive equilibrium where AI and trust mutually reinforce each other. The dissertation begins with an introduction to trust and AI in networked systems security in Chapter 1, followed by an overview of cybersecurity terminologies and game theory in Chapter 2. The main contributions are categorized into three areas: strategic penetration testing for trustworthy networked systems, autonomous zero-trust mechanisms for cyber resilience, and prescriptive governance frameworks for AI-powered networks. The first two parts delve into how AI enhances trust in network security, while the third part discusses how trust can improve AI utilization and adoption.Part I of the dissertation focuses on strategic penetration testing for trustworthy networked systems. In response to potential adversarial threats, penetration testing has emerged as a crucial solution for uncovering system vulnerabilities and assessing network security through authorized ethical attacks. In Chapter 3, we introduce a meta-game-based automated penetration testing framework (MEGA-PT), which incorporates micro tactic games for node-level local interactions and a macro strategy process for network-level attack chains. This dual-level modeling facilitates distributed, adaptive, collaborative, and rapid penetration testing. MEGA-PT establishes fundamental principles to guide future automated penetration testing and offers a strategic testing method to verify and enhance the trustworthiness of networked systems during the preparation stage. Furthermore, we propose the idea of agent-based penetration testing aims to create open source technology that can support the creation of a sustainable competition league in which fresh cybersecurity datasets are continually generated.Cyber attacks are evolving and becoming more targeted, which requires systems to be resilient and capable of autonomous response in the face of cyber incidents. In Part II, we propose several autonomous zero-trust mechanisms for cyber resilience. Chapter 4 presents a game-theoretic zero-trust authentication framework (GAZETA) designed to deter lateral movement penetration and dynamically adapt authentication policies. In Chapter 5, we introduce a scenario-agnostic zero-trust defense (SA-ZTD) for autonomous policy generation across diverse scenarios. Lastly, we propose a task-aware and resilient trust management scheme (E2ETrust) for Industrial Internet of Things (IoT) systems, leveraging a risk-sensitive approach to optimize task performance.After discussing how AI techniques (e.g., learning and game theory) enhance trust-based methods in network security, the third part examines the relationship from the other direction and discusses how trust improves the governance and adaptation of AI-powered systems. In Chapter 6, we investigate effective and ethical responsibility attribution in AI-induced systems, establishing a computational framework (CARE) using reflective equilibrium to guide AI governance policy-making. In Chapter 7, an AI liability insurance framework for medical devices is introduced, analyzing its role in advancing responsible AI adoption in healthcare and exploring the impact of data poisoning attacks and the moral hazard problem within the framework. These prescriptive governance frameworks aim to enhance the trustworthiness and responsible use of AI-powered systems.
546 $a English
590 $a School code: 1988
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer engineering. $3 569006
653 $a Accountability
653 $a AI governance
653 $a Cyber security
653 $a Game theory
653 $a Network security
653 $a Zero trust
690 $a 0544
690 $a 0464
690 $a 0800
710 2 $a New York University Tandon School of Engineering. $b Electrical & Computer Engineering. $3 1437750
720 1 $a Zhu, Quanyan $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-04B.
790 $a 1988
791 $a Ph.D.
792 $a 2024
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31559144