
Cybersecurity in Artificial Intelligence: Attacks Defenses and Real World Application
Anshuman Mishra
This audiobook is narrated by a digital voice.
In the era of rapid digital transformation, artificial intelligence (AI) has emerged as one of the most disruptive technologies across every conceivable industry. From healthcare and finance to smart cities and national defense, AI has dramatically reshaped the landscape of data processing, decision-making, and automation. However, as organizations and governments increasingly adopt AI, they must also confront an alarming reality: the rise of complex cybersecurity threats targeting and leveraging AI systems.
"Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications" is a comprehensive academic and professional course book that serves as a foundational and advanced guide to understanding the intersection of artificial intelligence and cybersecurity. This book is not merely a theoretical exploration—it is a hands-on, practical resource enriched with case studies, industry practices, emerging tools, and policy discussions that bridge the gap between AI development and cybersecurity preparedness.
AI, by its nature, is designed to learn, adapt, and make decisions based on data. However, this learning capability is also what makes AI systems vulnerable to manipulation, data poisoning, adversarial attacks, and systemic biases. While traditional cybersecurity mechanisms focus on securing data networks and IT infrastructure, AI systems introduce unique risks that require novel approaches, innovative frameworks, and intelligent countermeasures.
This book equips readers with the knowledge, tools, and skills necessary to understand and defend against the growing threats targeting AI systems. Whether you are a student, researcher, IT professional, ethical hacker, or corporate strategist, this book provides invaluable insights into building robust, ethical, and secure AI-driven systems that can withstand both current and future threats.
Purpose and Importance of the Book
Cybersecurity and artificial intelligence are no longer separate domains. The growing integration of AI into critical infrastructure and consumer products has given rise to new categories of threats—ranging from adversarial machine learning and model inversion to AI-driven malware and autonomous threat actors. Simultaneously, AI is also transforming the cybersecurity industry by enabling proactive threat detection, predictive analytics, and adaptive security policies.
The purpose of this book is threefold:
To EducateTo EquipTo Empower
Duration - 20h 32m.
Author - Anshuman Mishra.
Narrator - Digital Voice Madison G.
Published Date - Tuesday, 07 January 2025.
Copyright - © 2025 Anshuman Mishra ©.
Location:
United States
Description:
This audiobook is narrated by a digital voice. In the era of rapid digital transformation, artificial intelligence (AI) has emerged as one of the most disruptive technologies across every conceivable industry. From healthcare and finance to smart cities and national defense, AI has dramatically reshaped the landscape of data processing, decision-making, and automation. However, as organizations and governments increasingly adopt AI, they must also confront an alarming reality: the rise of complex cybersecurity threats targeting and leveraging AI systems. "Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications" is a comprehensive academic and professional course book that serves as a foundational and advanced guide to understanding the intersection of artificial intelligence and cybersecurity. This book is not merely a theoretical exploration—it is a hands-on, practical resource enriched with case studies, industry practices, emerging tools, and policy discussions that bridge the gap between AI development and cybersecurity preparedness. AI, by its nature, is designed to learn, adapt, and make decisions based on data. However, this learning capability is also what makes AI systems vulnerable to manipulation, data poisoning, adversarial attacks, and systemic biases. While traditional cybersecurity mechanisms focus on securing data networks and IT infrastructure, AI systems introduce unique risks that require novel approaches, innovative frameworks, and intelligent countermeasures. This book equips readers with the knowledge, tools, and skills necessary to understand and defend against the growing threats targeting AI systems. Whether you are a student, researcher, IT professional, ethical hacker, or corporate strategist, this book provides invaluable insights into building robust, ethical, and secure AI-driven systems that can withstand both current and future threats. Purpose and Importance of the Book Cybersecurity and artificial intelligence are no longer separate domains. The growing integration of AI into critical infrastructure and consumer products has given rise to new categories of threats—ranging from adversarial machine learning and model inversion to AI-driven malware and autonomous threat actors. Simultaneously, AI is also transforming the cybersecurity industry by enabling proactive threat detection, predictive analytics, and adaptive security policies. The purpose of this book is threefold: To EducateTo EquipTo Empower Duration - 20h 32m. Author - Anshuman Mishra. Narrator - Digital Voice Madison G. Published Date - Tuesday, 07 January 2025. Copyright - © 2025 Anshuman Mishra ©.
Language:
English
Anshuman Mishra
Duration:00:00:07
Book Title: | “Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications”
Duration:00:00:16
Table of Contents | 🔹 Unit 1: Foundations of AI and Cybersecurity
Duration:00:00:51
🔹 Unit 2: Threats and Vulnerabilities in AI Systems
Duration:00:01:03
🔹 Unit 3: AI in the Hands of Attackers
Duration:00:00:46
🔹 Unit 4: Defense Mechanisms for Securing AI
Duration:00:01:09
🔹 Unit 5: Advanced Applications and Industry Tools
Duration:00:00:51
🔹 Unit 6: Ethics, Policies, and Future Trends
Duration:00:01:27
Introduction
Duration:00:02:08
Purpose and Importance of the Book
Duration:00:01:23
Benefits of Studying This Book
Duration:00:03:28
Real-World Applications
Duration:00:01:01
The Reader’s Journey
Duration:00:01:04
Target Audience
Duration:00:00:47
Final Thoughts
Duration:00:03:52
1. Evolution and Branches of AI (ML, DL, NLP, RL)
Duration:00:21:43
2. Why AI Needs Cybersecurity
Duration:00:11:22
3. Attack Surface in Intelligent Systems
Duration:00:31:58
4. Case Study: Microsoft Tay Chatbot Shutdown (Adversarial User Input)
Duration:00:12:56
4. Case Study: Microsoft Tay Chatbot Shutdown
Duration:00:01:04
5. Learning Resources and Staying Updated
Duration:00:01:48
1. CIA Triad and Its Relevance in AI
Duration:00:15:43
2. Common Cyber Attacks (Malware, Phishing, DoS, Man-in-the-Middle)
Duration:00:15:01
3. Role of Cryptography and Hashing
Duration:00:16:44
4. Case Study: Equifax Data Breach – Weak AI-Driven Security Detection
Duration:00:17:06
1. Introduction to Data Poisoning and Training-Time Attacks
Duration:00:03:20
2. Types of Data Poisoning Attacks
Duration:00:21:08
3. General Mitigation Strategies for Training-Time Attacks
Duration:00:03:52
1. Data Quality Issues
Duration:00:06:10
2. Overfitting and Underfitting
Duration:00:02:33
3. Model Complexity and Generalization
Duration:00:02:13
4. Adversarial Attacks and Robustness
Duration:00:03:25
5. Data Leakage
Duration:00:01:32
6. Concept Drift and Data Distribution Shifts
Duration:00:02:28
7. Interpretability and Explainability
Duration:00:02:45
8. Ethical Considerations
Duration:00:02:58
1. Introduction to Trojan Attacks in Machine Learning
Duration:00:02:33
2. Case Study: Trojan Attack on an Image Classifier (Hypothetical Scenario)
Duration:00:04:14
3. Impact on Model Accuracy
Duration:00:02:31
4. Impact on Model Integrity
Duration:00:03:46
5. Detection and Mitigation Strategies
Duration:00:06:18
1. Types of Data Poisoning Attacks
Duration:00:01:44
2. Impact on Model Accuracy and Integrity
Duration:00:02:00
3. Case Study: Trojan Attack in Image Recognition Models
Duration:00:06:02
1. Fast Gradient Sign Method (FGSM)
Duration:00:05:45
2. Projected Gradient Descent (PGD)
Duration:00:09:10
3. Carlini-Wagner (C&W) Attack
Duration:00:09:06
4. Boundary Attack
Duration:00:08:38
5. Evasion vs. Poisoning vs. Extraction
Duration:00:00:36
5.1. Evasion Attacks
Duration:00:05:51
5.2. Poisoning Attacks
Duration:00:06:44
5.3. Model Extraction Attacks (Model Inversion/Stealing)
Duration:00:08:04
Summary of Differences:
Duration:00:00:03
6. Case Study: Fooling Traffic Sign Detection in Autonomous Cars
Duration:00:01:22
6.1. Significance and Threat Landscape
Duration:00:01:16
6.2. Attack Methodologies: From Digital to Physical
Duration:00:05:33
6.3. Practical Challenges and Implications
Duration:00:02:12
6.4. Defenses Against Adversarial Traffic Sign Attacks
Duration:00:04:13
6.5. Future Directions and Ongoing Research
Duration:00:01:31
🧠 1. Understanding Adversarial Attacks: FGSM, PGD, Carlini-Wagner, Boundary
Duration:00:02:03
🔐 2. Evasion vs. Poisoning vs. Extraction Attacks
Duration:00:02:01
🚗 3. Case Study: Fooling Traffic Sign Detection in Autonomous Cars
Duration:01:07:46
5. Case Study: Stealing Models from Open ML APIs (Google, Amazon)
Duration:00:22:26
MCQs on Model Inversion, Model Stealing, and Membership Inference
Duration:00:03:21
MCQs on Intellectual Property and Black-Box API Vulnerabilities
Duration:00:03:28
MCQs on Case Study: Stealing Models from Google & Amazon APIs
Duration:00:05:01
1. AI-Generated Phishing
Duration:00:06:21
2. AI-Driven Spear Phishing
Duration:00:07:02
3. Social Engineering Bots
Duration:00:06:37
General Countermeasures Against AI-Powered Social Engineering
Duration:00:07:41
4. Deepfakes and Synthetic Identity Generation
Duration:00:21:48
5. Case Study: DeepNude, Fake Celebrity Scandals & Political Disinformation
Duration:00:16:20
MCQs on AI-generated Phishing, Spear Phishing & Social Engineering Bots
Duration:00:03:33
MCQs on Deepfakes and Synthetic Identity Generation
Duration:00:03:28
MCQs on Case Study: DeepNude, Fake Celebrity Scandals & Political Disinformation
Duration:00:04:27
1. AI-Crafted Polymorphic Malware
Duration:00:08:55
2. Smart Ransomware and Botnets
Duration:00:16:20
3. AI for Automated Scanning and Payload Generation
Duration:00:19:12
4. Case Study: Emotet AI-based Malware Campaign
Duration:00:16:39
MCQs on AI-Crafted Polymorphic Malware
Duration:00:03:36
MCQs on Smart Ransomware and Botnets
Duration:00:03:32
MCQs on AI for Automated Scanning and Payload Generation
Duration:00:03:43
1. Defensive Distillation and Gradient Masking
Duration:00:11:17
2. Adversarial Training
Duration:00:10:36
3. Detection of Adversarial Inputs
Duration:00:15:29
4. Case Study: Robust AI in Financial Fraud Detection
Duration:00:18:47
MCQs on Defensive Distillation and Gradient Masking
Duration:00:03:33
MCQs on Adversarial Training
Duration:00:03:28
MCQs on Detection of Adversarial Inputs
Duration:00:03:53
Chapter 9: Securing the AI Lifecycle
Duration:00:02:32
1. Secure Data Collection, Storage, and Validation
Duration:00:12:28
2. Model Testing, Versioning, and Deployment Safeguards
Duration:00:17:07
3. Continuous Monitoring and Feedback Loops
Duration:00:24:34
4. Case Study: Uber’s AI Failure in Self-Driving Car Incident
Duration:00:20:34
Secure Data Collection, Storage, and Validation
Duration:00:01:50
Model Testing, Versioning, and Deployment Safeguards
Duration:00:01:54
Continuous Monitoring and Feedback Loops
Duration:00:01:54
Case Study: Uber’s AI Failure in Self-Driving Car Incident
Duration:00:05:42
1. The Imperative of Explainable and Trustworthy AI
Duration:00:02:55
2. Importance of Interpretability
Duration:00:04:55
3. LIME (Local Interpretable Model-agnostic Explanations)
Duration:00:06:38
4. SHAP (SHapley Additive exPlanations)
Duration:00:08:46
5. Bias Detection and Fairness Audits
Duration:00:12:40
6. Logging and Explainability for Compliance
Duration:00:13:50
7. Case Study: COMPAS Recidivism Prediction Bias Lawsuit
Duration:00:12:42
Interpretability: LIME, SHAP, and Model Transparency
Duration:00:03:39
Bias Detection and Fairness Audits
Duration:00:03:42
Logging and Explainability for Compliance
Duration:00:03:47
1. The Evolving Landscape of AI Security
Duration:00:03:22
2. TensorFlow Privacy
Duration:00:07:20
3. CleverHans
Duration:00:06:57
4. IBM ART (Adversarial Robustness Toolbox)
Duration:00:07:09
5. Use of Metasploit, Wireshark, and Kali Linux for AI Apps
Duration:00:00:47
6. Metasploit for AI Applications
Duration:00:05:13
7. Wireshark for AI Network Traffic Analysis
Duration:00:04:47
8. Kali Linux for AI Security Testing
Duration:00:05:11
9. Secure AI Pipelines with MLOps
Duration:00:13:23
10. Case Study: Red Teaming AI Pipelines in Healthcare
Duration:00:16:06
TensorFlow Privacy, CleverHans, IBM ART
Duration:00:03:11
Use of Metasploit, Wireshark, and Kali Linux for AI Applications
Duration:00:02:59
Secure AI Pipelines with MLOps
Duration:00:01:09
1. The Confluence of AI, Blockchain, IoT, and Quantum Computing
Duration:00:04:48
2. AI + Blockchain for Secure Identity and Data Integrity
Duration:00:11:08
3. Securing AI in IoT Environments
Duration:00:12:00
3. Quantum Attacks on Encryption and Model Privacy
Duration:00:15:18
4. Case Study: Smart Home Breaches via Voice AI Assistants
Duration:00:15:40
AI + Blockchain for Secure Identity and Data Integrity
Duration:00:03:52
Securing AI in IoT Environments
Duration:00:08:18
1. Data Privacy Regulations and Their Impact on AI
Duration:00:26:15
2. AI Risk Frameworks
Duration:00:17:35
3. Ethical Hacking and Red Teaming in AI
Duration:00:16:00
4. Case Study: The Facebook-Cambridge Analytica Scandal
Duration:00:19:02
Data Privacy Laws
Duration:00:03:35
AI Risk Frameworks
Duration:00:05:24
1. Roles in AI Cybersecurity: Navigating a New Frontier
Duration:00:18:08
2. Key Skills and Certifications for AI Cybersecurity
Duration:00:12:44
3. Learning Roadmap and Project Ideas for AI Cybersecurity
Duration:00:19:44
4. Mini Insights from Industry Professionals (Synthesized)
Duration:00:11:03
Roles & Responsibilities
Duration:00:01:29
Key Skills & Certifications
Duration:00:01:15
Learning Roadmap & Project Ideas
Duration:00:01:14
Mini Interviews: Insights from Industry Professionals
Duration:00:01:23
Advanced Concepts
Duration:00:01:13
Emerging Trends
Duration:00:06:38
1. The Evolving Threat Landscape and the Need for AI in Cybersecurity
Duration:00:02:36
2. The Foundational Role of AI and Machine Learning in Cybersecurity
Duration:00:03:23
3. Autonomous Cyber Defense with AI
Duration:00:09:57
4. Predictive Threat Intelligence Using AI
Duration:00:08:42
5. Ethical Considerations of AI in Cybersecurity
Duration:00:08:58
6. The Impact of Quantum Computing on AI Cybersecurity
Duration:00:06:20
7. Regulatory Landscape for AI in Cybersecurity
Duration:00:07:26
8. Integration of Generative AI with Security Operations
Duration:00:16:51
9. Integration of Generative AI with Security Operations
Duration:00:16:51
10. Case Study: Generative AI Prompt Injection Attacks on Chatbots
Duration:00:16:44
Autonomous Cyber Defense with AI
Duration:00:01:22
Predictive Threat Intelligence Using AI
Duration:00:06:25