Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.

While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).

Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:

This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.

While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).

Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:

This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:This is the first book to explain various practical attacks and countermeasures to AI systemsBoth quantitative math models and practical security implementations are providedIt covers both "securing the AI system itself" and "using AI to achieve security"It covers all the advanced AI attacks and threats with detailed attack modelsIt provides multiple solution spaces to the security and privacy issues in AI toolsThe differences among ML and DL security and privacy issues are explainedMany practical security applications are covered

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:This is the first book to explain various practical attacks and countermeasures to AI systemsBoth quantitative math models and practical security implementations are providedIt covers both "securing the AI system itself" and "using AI to achieve security"It covers all the advanced AI attacks and threats with detailed attack modelsIt provides multiple solution spaces to the security and privacy issues in AI toolsThe differences among ML and DL security and privacy issues are explainedMany practical security applications are covered

In the treatment of chronic diseases, wireless Implantable Medical Devices (IMDs) are commonly used to communicate with an outside programmer (reader). Such communication raises serious security concerns, such as the ability for hackers to gain access to a patient’s medical records. This brief provides an overview of such attacks and the new security challenges, defenses, design issues, modeling and performance evaluation in wireless IMDs.  While studying the vulnerabilities of IMDs and corresponding security defenses, the reader will also learn the methodologies and tools for designing security schemes, modeling, security analysis, and performance evaluation, thus keeping pace with quickly-evolving wireless security research.

In the treatment of chronic diseases, wireless Implantable Medical Devices (IMDs) are commonly used to communicate with an outside programmer (reader). Such communication raises serious security concerns, such as the ability for hackers to gain access to a patient’s medical records. This brief provides an overview of such attacks and the new security challenges, defenses, design issues, modeling and performance evaluation in wireless IMDs.  While studying the vulnerabilities of IMDs and corresponding security defenses, the reader will also learn the methodologies and tools for designing security schemes, modeling, security analysis, and performance evaluation, thus keeping pace with quickly-evolving wireless security research.

This book LNICST 622 constitutes the refereed proceedings of the Second EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles, SmartSP 2024, held in New Orleans, LA, USA, during November 7–8, 2024.The 18 full papers were carefully reviewed an selected from 47 submissions. The proceedings focus on Emerging Applications, Hardware and Firmware Security, Adversarial Attacks in Autonomous Systems, Ethics, Privacy, Human-Centric Considerations and Security Techniques for Cyber-Physical Systems.

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.

While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).

Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:

This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.

While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).

Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:

This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:This is the first book to explain various practical attacks and countermeasures to AI systemsBoth quantitative math models and practical security implementations are providedIt covers both "securing the AI system itself" and "using AI to achieve security"It covers all the advanced AI attacks and threats with detailed attack modelsIt provides multiple solution spaces to the security and privacy issues in AI toolsThe differences among ML and DL security and privacy issues are explainedMany practical security applications are covered

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:This is the first book to explain various practical attacks and countermeasures to AI systemsBoth quantitative math models and practical security implementations are providedIt covers both "securing the AI system itself" and "using AI to achieve security"It covers all the advanced AI attacks and threats with detailed attack modelsIt provides multiple solution spaces to the security and privacy issues in AI toolsThe differences among ML and DL security and privacy issues are explainedMany practical security applications are covered

In the treatment of chronic diseases, wireless Implantable Medical Devices (IMDs) are commonly used to communicate with an outside programmer (reader). Such communication raises serious security concerns, such as the ability for hackers to gain access to a patient’s medical records. This brief provides an overview of such attacks and the new security challenges, defenses, design issues, modeling and performance evaluation in wireless IMDs.  While studying the vulnerabilities of IMDs and corresponding security defenses, the reader will also learn the methodologies and tools for designing security schemes, modeling, security analysis, and performance evaluation, thus keeping pace with quickly-evolving wireless security research.

In the treatment of chronic diseases, wireless Implantable Medical Devices (IMDs) are commonly used to communicate with an outside programmer (reader). Such communication raises serious security concerns, such as the ability for hackers to gain access to a patient’s medical records. This brief provides an overview of such attacks and the new security challenges, defenses, design issues, modeling and performance evaluation in wireless IMDs.  While studying the vulnerabilities of IMDs and corresponding security defenses, the reader will also learn the methodologies and tools for designing security schemes, modeling, security analysis, and performance evaluation, thus keeping pace with quickly-evolving wireless security research.

This book LNICST 622 constitutes the refereed proceedings of the Second EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles, SmartSP 2024, held in New Orleans, LA, USA, during November 7–8, 2024.The 18 full papers were carefully reviewed an selected from 47 submissions. The proceedings focus on Emerging Applications, Hardware and Firmware Security, Adversarial Attacks in Autonomous Systems, Ethics, Privacy, Human-Centric Considerations and Security Techniques for Cyber-Physical Systems.