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...

Premium Chapters

Premium

Anshuman Mishra

1/6/2025

Book Title: | “Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications”

1/6/2025

Table of Contents | 🔹 Unit 1: Foundations of AI and Cybersecurity

1/6/2025

🔹 Unit 2: Threats and Vulnerabilities in AI Systems

1/6/2025

🔹 Unit 3: AI in the Hands of Attackers

1/6/2025

🔹 Unit 4: Defense Mechanisms for Securing AI

1/6/2025

🔹 Unit 5: Advanced Applications and Industry Tools

1/6/2025

🔹 Unit 6: Ethics, Policies, and Future Trends

1/6/2025

Introduction

1/6/2025

Purpose and Importance of the Book

1/6/2025

Benefits of Studying This Book

1/6/2025

Real-World Applications

1/6/2025

The Reader’s Journey

1/6/2025

Target Audience

1/6/2025

Final Thoughts

1/6/2025

1. Evolution and Branches of AI (ML, DL, NLP, RL)

1/6/2025

2. Why AI Needs Cybersecurity

1/6/2025

3. Attack Surface in Intelligent Systems

1/6/2025

4. Case Study: Microsoft Tay Chatbot Shutdown (Adversarial User Input)

1/6/2025

4. Case Study: Microsoft Tay Chatbot Shutdown

1/6/2025

5. Learning Resources and Staying Updated

1/6/2025

1. CIA Triad and Its Relevance in AI

1/6/2025

2. Common Cyber Attacks (Malware, Phishing, DoS, Man-in-the-Middle)

1/6/2025

3. Role of Cryptography and Hashing

1/6/2025

4. Case Study: Equifax Data Breach – Weak AI-Driven Security Detection

1/6/2025

1. Introduction to Data Poisoning and Training-Time Attacks

1/6/2025

2. Types of Data Poisoning Attacks

1/6/2025

3. General Mitigation Strategies for Training-Time Attacks

1/6/2025

1. Data Quality Issues

1/6/2025

2. Overfitting and Underfitting

1/6/2025

3. Model Complexity and Generalization

1/6/2025

4. Adversarial Attacks and Robustness

1/6/2025

5. Data Leakage

1/6/2025

6. Concept Drift and Data Distribution Shifts

1/6/2025

7. Interpretability and Explainability

1/6/2025

8. Ethical Considerations

1/6/2025

1. Introduction to Trojan Attacks in Machine Learning

1/6/2025

2. Case Study: Trojan Attack on an Image Classifier (Hypothetical Scenario)

1/6/2025

3. Impact on Model Accuracy

1/6/2025

4. Impact on Model Integrity

1/6/2025

5. Detection and Mitigation Strategies

1/6/2025

1. Types of Data Poisoning Attacks

1/6/2025

2. Impact on Model Accuracy and Integrity

1/6/2025

3. Case Study: Trojan Attack in Image Recognition Models

1/6/2025

1. Fast Gradient Sign Method (FGSM)

1/6/2025

2. Projected Gradient Descent (PGD)

1/6/2025

3. Carlini-Wagner (C&W) Attack

1/6/2025

4. Boundary Attack

1/6/2025

5. Evasion vs. Poisoning vs. Extraction

1/6/2025

5.1. Evasion Attacks

1/6/2025

5.2. Poisoning Attacks

1/6/2025

5.3. Model Extraction Attacks (Model Inversion/Stealing)

1/6/2025

Summary of Differences:

1/6/2025

6. Case Study: Fooling Traffic Sign Detection in Autonomous Cars

1/6/2025

6.1. Significance and Threat Landscape

1/6/2025

6.2. Attack Methodologies: From Digital to Physical

1/6/2025

6.3. Practical Challenges and Implications

1/6/2025

6.4. Defenses Against Adversarial Traffic Sign Attacks

1/6/2025

6.5. Future Directions and Ongoing Research

1/6/2025

🧠 1. Understanding Adversarial Attacks: FGSM, PGD, Carlini-Wagner, Boundary

1/6/2025

🔐 2. Evasion vs. Poisoning vs. Extraction Attacks

1/6/2025

🚗 3. Case Study: Fooling Traffic Sign Detection in Autonomous Cars

1/6/2025

5. Case Study: Stealing Models from Open ML APIs (Google, Amazon)

1/6/2025

MCQs on Model Inversion, Model Stealing, and Membership Inference

1/6/2025

MCQs on Intellectual Property and Black-Box API Vulnerabilities

1/6/2025

MCQs on Case Study: Stealing Models from Google & Amazon APIs

1/6/2025

1. AI-Generated Phishing

1/6/2025

2. AI-Driven Spear Phishing

1/6/2025

3. Social Engineering Bots

1/6/2025

General Countermeasures Against AI-Powered Social Engineering

1/6/2025

4. Deepfakes and Synthetic Identity Generation

1/6/2025

5. Case Study: DeepNude, Fake Celebrity Scandals & Political Disinformation

1/6/2025

MCQs on AI-generated Phishing, Spear Phishing & Social Engineering Bots

1/6/2025

MCQs on Deepfakes and Synthetic Identity Generation

1/6/2025

MCQs on Case Study: DeepNude, Fake Celebrity Scandals & Political Disinformation

1/6/2025

1. AI-Crafted Polymorphic Malware

1/6/2025

2. Smart Ransomware and Botnets

1/6/2025

3. AI for Automated Scanning and Payload Generation

1/6/2025

4. Case Study: Emotet AI-based Malware Campaign

1/6/2025

MCQs on AI-Crafted Polymorphic Malware

1/6/2025

MCQs on Smart Ransomware and Botnets

1/6/2025

MCQs on AI for Automated Scanning and Payload Generation

1/6/2025

1. Defensive Distillation and Gradient Masking

1/6/2025

2. Adversarial Training

1/6/2025

3. Detection of Adversarial Inputs

1/6/2025

4. Case Study: Robust AI in Financial Fraud Detection

1/6/2025

MCQs on Defensive Distillation and Gradient Masking

1/6/2025

MCQs on Adversarial Training

1/6/2025

MCQs on Detection of Adversarial Inputs

1/6/2025

Chapter 9: Securing the AI Lifecycle

1/6/2025

1. Secure Data Collection, Storage, and Validation

1/6/2025

2. Model Testing, Versioning, and Deployment Safeguards

1/6/2025

3. Continuous Monitoring and Feedback Loops

1/6/2025

4. Case Study: Uber’s AI Failure in Self-Driving Car Incident

1/6/2025

Secure Data Collection, Storage, and Validation

1/6/2025

Model Testing, Versioning, and Deployment Safeguards

1/6/2025

Continuous Monitoring and Feedback Loops

1/6/2025

Case Study: Uber’s AI Failure in Self-Driving Car Incident

1/6/2025

1. The Imperative of Explainable and Trustworthy AI

1/6/2025

2. Importance of Interpretability

1/6/2025

3. LIME (Local Interpretable Model-agnostic Explanations)

1/6/2025

4. SHAP (SHapley Additive exPlanations)

1/6/2025

5. Bias Detection and Fairness Audits

1/6/2025

6. Logging and Explainability for Compliance

1/6/2025

7. Case Study: COMPAS Recidivism Prediction Bias Lawsuit

1/6/2025

Interpretability: LIME, SHAP, and Model Transparency

1/6/2025

Bias Detection and Fairness Audits

1/6/2025

Logging and Explainability for Compliance

1/6/2025

1. The Evolving Landscape of AI Security

1/6/2025

2. TensorFlow Privacy

1/6/2025

3. CleverHans

1/6/2025

4. IBM ART (Adversarial Robustness Toolbox)

1/6/2025

5. Use of Metasploit, Wireshark, and Kali Linux for AI Apps

1/6/2025

6. Metasploit for AI Applications

1/6/2025

7. Wireshark for AI Network Traffic Analysis

1/6/2025

8. Kali Linux for AI Security Testing

1/6/2025

9. Secure AI Pipelines with MLOps

1/6/2025

10. Case Study: Red Teaming AI Pipelines in Healthcare

1/6/2025

TensorFlow Privacy, CleverHans, IBM ART

1/6/2025

Use of Metasploit, Wireshark, and Kali Linux for AI Applications

1/6/2025

Secure AI Pipelines with MLOps

1/6/2025

1. The Confluence of AI, Blockchain, IoT, and Quantum Computing

1/6/2025

2. AI + Blockchain for Secure Identity and Data Integrity

1/6/2025

3. Securing AI in IoT Environments

1/6/2025

3. Quantum Attacks on Encryption and Model Privacy

1/6/2025

4. Case Study: Smart Home Breaches via Voice AI Assistants

1/6/2025

AI + Blockchain for Secure Identity and Data Integrity

1/6/2025

Securing AI in IoT Environments

1/6/2025

1. Data Privacy Regulations and Their Impact on AI

1/6/2025

2. AI Risk Frameworks

1/6/2025

3. Ethical Hacking and Red Teaming in AI

1/6/2025

4. Case Study: The Facebook-Cambridge Analytica Scandal

1/6/2025

Data Privacy Laws

1/6/2025

AI Risk Frameworks

1/6/2025

1. Roles in AI Cybersecurity: Navigating a New Frontier

1/6/2025

2. Key Skills and Certifications for AI Cybersecurity

1/6/2025

3. Learning Roadmap and Project Ideas for AI Cybersecurity

1/6/2025

4. Mini Insights from Industry Professionals (Synthesized)

1/6/2025

Roles & Responsibilities

1/6/2025

Key Skills & Certifications

1/6/2025

Learning Roadmap & Project Ideas

1/6/2025

Mini Interviews: Insights from Industry Professionals

1/6/2025

Advanced Concepts

1/6/2025

Emerging Trends

1/6/2025

1. The Evolving Threat Landscape and the Need for AI in Cybersecurity

1/6/2025

2. The Foundational Role of AI and Machine Learning in Cybersecurity

1/6/2025

3. Autonomous Cyber Defense with AI

1/6/2025

4. Predictive Threat Intelligence Using AI

1/6/2025

5. Ethical Considerations of AI in Cybersecurity

1/6/2025

6. The Impact of Quantum Computing on AI Cybersecurity

1/6/2025

7. Regulatory Landscape for AI in Cybersecurity

1/6/2025

8. Integration of Generative AI with Security Operations

1/6/2025

9. Integration of Generative AI with Security Operations

1/6/2025

10. Case Study: Generative AI Prompt Injection Attacks on Chatbots

1/6/2025

Autonomous Cyber Defense with AI

1/6/2025

Predictive Threat Intelligence Using AI

1/6/2025