AED
UAD Dirham
Code: AED
Mentor Based
Ethical Hacking and AI Security
Secure the Future: Master AI-Driven Ethical Hacking and Cybersecurity
Early access to e-LMS included
About This Course
The program covers key concepts such as AI-driven vulnerability assessment, network security, and AI security risk mitigation. It combines traditional ethical hacking techniques with AI-driven approaches to provide a comprehensive understanding of security in AI-driven environments.
Aim
This program teaches the application of AI in ethical hacking and cybersecurity, providing professionals with the skills to use AI tools to identify, analyze, and mitigate security threats. Participants will also learn to safeguard AI systems from cyber-attacks.
Program Objectives
- Understand ethical hacking techniques enhanced with AI tools.
- Learn to identify and mitigate security risks in AI models.
- Apply AI-driven vulnerability assessments and penetration testing.
- Build robust security frameworks to protect AI systems.
- Gain real-world experience in AI-driven cybersecurity.
Program Structure
-
Module 1: Introduction to Ethical Hacking and AI Security
Chapter 1: Overview of Cybersecurity and Ethical Hacking
- Lesson 1.1: Introduction to Cybersecurity
- Lesson 1.2: Ethical Hacking: Principles and Practices
- Lesson 1.3: The Role of Ethical Hacking in Enhancing Security
- Lesson 1.4: Real-World Examples of Ethical Hacking in Cybersecurity
Chapter 2: Introduction to AI in Cybersecurity: Opportunities and Risks
- Lesson 2.1: AI’s Role in Modern Cybersecurity
- Lesson 2.2: Opportunities AI Brings to Cybersecurity
- Lesson 2.3: Risks and Challenges in Using AI for Cybersecurity
- Lesson 2.4: Case Studies: Success and Failures of AI in Cybersecurity
Module 2: Legal and Ethical Frameworks for Ethical Hacking
Chapter 1: Legal Aspects of Ethical Hacking
- Lesson 1.1: Understanding Cybersecurity Laws and Regulations
- Lesson 1.2: The Ethics of Hacking: Legal Boundaries and Responsibilities
- Lesson 1.3: Key Regulations Governing Ethical Hacking Globally
Chapter 2: Ethical Frameworks in AI and Cybersecurity
- Lesson 2.1: AI and Ethics: A Broad Overview
- Lesson 2.2: Ensuring Ethical AI Implementation in Security
- Lesson 2.3: Ethical Considerations in AI-Powered Cybersecurity Tools
Module 3: AI-Driven Threat Detection
Chapter 1: AI Techniques for Identifying Cyber Threats
- Lesson 1.1: AI in Threat Detection: An Overview
- Lesson 1.2: Machine Learning Algorithms for Cyber Threat Detection
- Lesson 1.3: Advanced Threat Detection Using Deep Learning
- Lesson 1.4: Real-World AI-Driven Threat Detection Examples
Chapter 2: Intrusion Detection Systems (IDS) with Machine Learning
- Lesson 2.1: Introduction to IDS and Its Role in Cybersecurity
- Lesson 2.2: Machine Learning-Based IDS Models
- Lesson 2.3: Enhancing IDS with AI for Real-Time Threat Detection
Chapter 3: Case Studies: AI in Malware Detection and Network Security
- Lesson 3.1: AI for Malware Detection: How It Works
- Lesson 3.2: AI-Powered Network Security Solutions
- Lesson 3.3: Case Studies of AI-Enhanced Network Security
Module 4: Penetration Testing with AI
Chapter 1: Overview of Penetration Testing (Pen Testing)
- Lesson 1.1: Introduction to Pen Testing: Purpose and Scope
- Lesson 1.2: The Stages of a Pen Test: A Detailed Breakdown
- Lesson 1.3: Pen Testing Methodologies and Standards
Chapter 2: Using AI to Automate Pen Testing and Vulnerability Assessment
- Lesson 2.1: The Role of AI in Automating Pen Testing
- Lesson 2.2: AI for Vulnerability Identification and Prioritization
- Lesson 2.3: Real-World Applications of AI-Driven Pen Testing
Chapter 3: Tools and Techniques for AI-Driven Pen Testing
- Lesson 3.1: Tools for AI-Enabled Pen Testing
- Lesson 3.2: Advanced AI Techniques for Exploiting Vulnerabilities
- Lesson 3.3: Case Study: Automated Vulnerability Assessment with AI
Module 5: Exploiting Vulnerabilities in AI Systems
Chapter 1: AI Model Vulnerabilities: Adversarial Attacks and Model Stealing
- Lesson 1.1: Understanding AI Model Vulnerabilities
- Lesson 1.2: Adversarial Attacks: Techniques and Implications
- Lesson 1.3: Model Stealing Attacks: How They Work
- Lesson 1.4: Case Studies of Adversarial and Model Stealing Attacks
Chapter 2: Evasion and Poisoning Attacks on Machine Learning Models
- Lesson 2.1: Evasion Attacks: Overview and Strategies
- Lesson 2.2: Poisoning Attacks: Methods and Prevention
- Lesson 2.3: Defending Against Adversarial and Poisoning Attacks
Module 6: AI for Phishing Detection and Prevention
Chapter 1: AI Techniques for Detecting Phishing Attacks
- Lesson 1.1: Introduction to Phishing Detection
- Lesson 1.2: Machine Learning Techniques for Phishing Detection
- Lesson 1.3: AI-Based Tools for Anti-Phishing
Chapter 2: Real-Time Phishing Detection Using Machine Learning
- Lesson 2.1: Real-Time Threat Detection with AI
- Lesson 2.2: Case Study: AI-Powered Phishing Detection in Action
- Lesson 2.3: Emerging Technologies in Phishing Prevention
Module 7: Adversarial Machine Learning
Chapter 1: Introduction to Adversarial Attacks: Evasion, Poisoning, and Model Inversion
- Lesson 1.1: Introduction to Adversarial Machine Learning
- Lesson 1.2: Evasion Attacks: How They Impact Models
- Lesson 1.3: Poisoning and Model Inversion Attacks Explained
Chapter 2: Defense Mechanisms: Adversarial Training and Robustness
- Lesson 2.1: Adversarial Training: Strengthening AI Models
- Lesson 2.2: Improving Robustness in AI Systems
- Lesson 2.3: Emerging Defense Mechanisms
Chapter 3: Case Studies: Adversarial Attacks in Computer Vision and NLP
- Lesson 3.1: AI Vulnerabilities in Computer Vision
- Lesson 3.2: NLP Models and Adversarial Threats
- Lesson 3.3: Real-World Examples of AI Defense
Module 8: AI for Securing IoT and Smart Devices
Chapter 1: Security Challenges in IoT and Edge Devices
- Lesson 1.1: Understanding IoT Security
- Lesson 1.2: Common IoT Vulnerabilities and Threats
- Lesson 1.3: AI’s Role in Securing IoT
Chapter 2: AI-Driven Security Solutions for IoT Networks
- Lesson 2.1: AI Tools for IoT Security
- Lesson 2.2: Real-World Applications of AI in IoT
- Lesson 2.3: AI-Powered Threat Detection in IoT Devices
Chapter 3: Ethical Hacking for IoT: Identifying and Exploiting IoT Vulnerabilities
- Lesson 3.1: Ethical Hacking for IoT Networks
- Lesson 3.2: AI for Identifying IoT Vulnerabilities
- Lesson 3.3: Case Study: IoT Security Exploits
Module 9: AI in Network Security and Traffic Analysis
Chapter 1: AI for Monitoring and Analyzing Network Traffic
- Lesson 1.1: Network Traffic Monitoring: AI Tools and Techniques
- Lesson 1.2: AI in Network Behavior Analysis
- Lesson 1.3: Case Studies of AI for Network Traffic Security
Chapter 2: AI-Driven Firewalls and Intrusion Prevention Systems (IPS)
- Lesson 2.1: AI-Powered Firewalls: How They Work
- Lesson 2.2: Intrusion Prevention Systems with AI
- Lesson 2.3: Real-World Applications of AI-Driven Firewalls
Module 10: Privacy and AI Security
Chapter 1: Ensuring Data Privacy in AI-Driven Systems
- Lesson 1.1: Data Privacy Concerns in AI Systems
- Lesson 1.2: Strategies for Ensuring Privacy in AI
Chapter 2: Privacy-Preserving AI: Homomorphic Encryption, Differential Privacy
- Lesson 2.1: Homomorphic Encryption in AI
- Lesson 2.2: Differential Privacy: How It Protects Data
Chapter 3: Ethical Implications of AI in Data Privacy and Security
- Lesson 3.1: Balancing AI Innovation with Privacy
- Lesson 3.2: Ethical Dilemmas in AI-Powered Data Privacy
Module 11: Blockchain and AI for Cybersecurity
Chapter 1: Using AI and Blockchain to Enhance Security
- Lesson 1.1: Introduction to Blockchain for Cybersecurity
- Lesson 1.2: AI’s Role in Enhancing Blockchain Security
- Lesson 1.3: Case Studies of Blockchain-AI Integration for Cybersecurity
Chapter 2: AI-Driven Smart Contracts and Secure Transactions
- Lesson 2.1: AI-Powered Smart Contracts
- Lesson 2.2: Securing Blockchain Transactions with AI
Module 12: Defending AI Models Against Attacks
Chapter 1: Best Practices for Building Secure AI Models
- Lesson 1.1: Secure AI Development Practices
- Lesson 1.2: Key Considerations for AI Model Security
Chapter 2: AI Model Monitoring and Defense Mechanisms
- Lesson 2.1: Continuous Monitoring for AI Security
- Lesson 2.2: Automated Defense Mechanisms for AI Systems
Who Should Enrol?
Cybersecurity professionals, AI engineers, ethical hackers, and security analysts.
Program Outcomes
- Expertise in applying AI tools to ethical hacking and security.
- Skills in identifying and mitigating vulnerabilities in AI systems.
- Knowledge of AI-driven threat detection and penetration testing.
- Ability to build secure AI models and systems against adversarial attacks.
Fee Structure
Discounted: ₹8499 | $112
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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