Mentor Based
Ethical Hacking and AI Security
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Course Overview
Program Syllabus
Module 1
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.
Module 2
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.
Module 3
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.
Module 4
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
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
Ethical Hacking and AI Security
Professional Certification Program
π₯
FormatLive + Recorded
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Duration8 Weeks
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CertificationVerified
Module 5
Chapter 2: Introduction to AI in Cybersecurity: Opportunities and Risks
- Lesson 2.1: AIβs 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 6
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
Module 7
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 8
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
Module 9
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
Module 10
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 11
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
Module 12
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
Module 13
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 14
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
Module 15
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 16
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
Module 17
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 18
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
Module 19
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
Module 20
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 21
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: AIβs Role in Securing IoT
Module 22
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
Module 23
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 24
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
Module 25
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 26
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
Module 27
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
Module 28
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 29
Chapter 1: Using AI and Blockchain to Enhance Security
- Lesson 1.1: Introduction to Blockchain for Cybersecurity
- Lesson 1.2: AIβs Role in Enhancing Blockchain Security
- Lesson 1.3: Case Studies of Blockchain-AI Integration for Cybersecurity
Module 30
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 31
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
Module 32
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
Module 33
Who Should Enrol?
Cybersecurity professionals, AI engineers, ethical hackers, and security analysts.
Module 34
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.
Module 36
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
Module 37
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 38
Publication Opportunity
Get published in a prestigious open-access journal.
Module 39
Centre of Excellence
Become part of an elite research community.
Module 40
Networking & Learning
Connect with global researchers and mentors.
Module 41
Global Recognition
Worth βΉ20,000 / $1,000 in academic value.
Module 42
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