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AI+ Ethical Hacker™ Self Paced Learning V3
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Introducción
Course Introduction
Lab Instruction for Python Based
Lab Instruction for VM Security Tool Based
Audio Book: Introduction AI+ Ethical Hacker
Module 1: Foundation of Ethical Hacking using Artificial Intelligence (AI)
Module 1: Foundation of Ethical Hacking Using Artificial Intelligence (AI)
Audio Book: Foundation of Ethical Hacking using Artificial Intelligence (AI)
1.1 Introduction to Ethical Hacking: Roles and Responsibilities
1.2 Ethical Hacking: Knowledge, Skills, Tools and Techniques
Activity 1
1.3 Ethical Hacking Methodology
Activity 2
1.4 Legal and Regulatory Framework
Activity 3
1.5 Hacker Types and Motivations
Activity 4
1.6 Information Gathering, Footprinting and Reconnaissance
Activity 5
1.7 Types of Network Scanning
1.8 Common Scanning Tools
Activity 6
1.9 Enumeration: Importance, Distinction, and Process
1.10 Port Scanning and Comprehensive Enumeration Techniques
Activity 7
Activity 8
Quiz
Lab Practice 1.1 -VM Tool Based
Lab Practice 1.2 - VM Tool Based
Lab Practice 1.3 - Python Code Based
Python File Download
Module 2: Introduction to AI in Ethical Hacking
Module 2: Introduction to AI in Ethical Hacking
Audio Book: Introduction to AI in Ethical Hacking
2.1 The Role of AI in Ethical Hacking
2.2 Real-World Applications and Case Studies
2.3 Machine Learning
2.4 Neural Networks
2.5 Advanced NLP Techniques: Emotion AI and Information Extraction
Activity 1
2.6 Computer Vision: A Detailed Exploration with Case Studies
2.7 Deep Learning
2.8 Reinforcement Learning
Activity 2
2.9 Machine Learning in Cybersecurity
Activity 3
2.10 Natural Language Processing (NLP) for Cybersecurity
Activity 4
2.11 Deep Learning for Threat Detection
Activity 5
2.12 Adversarial Machine Learning in Cybersecurity
Activity 6
2.13 AI-Driven Threat Intelligence Platforms
2.14 Case Studies and Future Trends
Activity 7
2.15 Cybersecurity Automation with AI
Activity 8
Activity 9
Quiz
Lab Practice 2.1 - VM Tool Based
Lab Practice 2.2 - Python Code Based
Download Dataset - Lab Practice 2.2
Lab Practice 2.3 - Python Code Based
Python File Download
Module 3: AI Tools and Technologies in Ethical Hacking
Module 3: AI Tools and Technologies in Ethical Hacking
Audio Book: AI Tools and Technologies in Ethical Hacking
3.1 AI-based Threat Detection Tools
Activity 1
3.2 Popular Machine Learning Frameworks for Ethical Hacking
Activity 2
3.3 AI-Enhanced Penetration Testing Tools
Activity 3
3.4 Behavioral Analysis for Anomaly Detection
3.5 Techniques Used in Behavioral Analysis
3.6 Applications, Benefits, and Limitations of Behavioral Analysis in Ethical Hacking
Activity 4
3.7 AI-Driven Network Security Solutions
Activity 5
3.8 Automated Vulnerability Scanners and Types
3.9 Key Features, Benefits, and Limitations of Automated Vulnerability Scanners
3.10 Popular Automated Vulnerability Scanners
Activity 6
3.11 AI in Web Application
Activity 7
3.12 AI for Malware Detection and Analysis
Activity 8
3.13 Cognitive Security Tools
Activity 9
Activity 10
Quiz
Lab Practice 3.1 - VM Tool Based
Lab Practice 3.2 - Python Code Based
Python File Download
Module 4: AI-Driven Reconnaissance Techniques
Module 4: AI-Driven Reconnaissance Techniques
Audio Book: AI-Driven Reconnaissance Techniques
4.1 Introduction to Reconnaissance in Ethical Hacking
Activity 1
4.2 Traditional vs. AI-Driven Reconnaissance
Activity 2
4.3 OS Fingerprinting: Importance and Traditional Techniques
4.4 AI-Powered OS Fingerprinting Techniques
Activity 3
4.5 AI-Enhanced Port Scanning Techniques
Activity 4
4.6 Machine Learning for Network Mapping
Activity 5
4.7 AI-Driven Social Engineering Reconnaissance
Activity 6
4.8 Machine Learning in OSINT
Activity 7
4.9 AI-Enhanced DNS Enumeration and AI-Driven Target Profiling
Activity 8
Activity 9
Quiz
Lab Practice 4.1 - VM Tool Based
Lab Practice 4.2 - Python Code Based
Lab Practice 4.3 - Python Code Based
Python File Download
Module 5: AI in Vulnerability Assessment and Penetration Testing
Module 5: AI in Vulnerability Assessment and Penetration Testing
Audio Book: AI in Vulnerability Assessment and Penetration Testing
5.1 Automated Vulnerability Scanning with AI
Activity 1
5.2 Machine Learning in Penetration Testing
5.3 Automated Vulnerability Analysis
5.4 Predictive Analysis and Threat Modeling
5.5 AI-Assisted Reporting and Remediation
5.6 Limitations and Challenges
Activity 2
5.7 Fundamentals of Machine Learning
5.8 Exploitation Techniques
5.9 Evaluation and Limitations of ML-Based Exploitation Techniques
Activity 3
5.10 Dynamic Application Security Testing (DAST): Overview
5.11 Applications and Benefits of AI in DAST
Activity 4
5.12 Fuzz Testing: A Brief Overview
5.13 AI-Driven Fuzz Testing: How It Works
Activity 5
5.14 Adversarial Machine Learning Techniques
5.15 Evaluating Security Systems Using Adversarial Machine Learning
Activity 6
5.16 Automated Report Generation using AI
Activity 7
5.17 AI-Based Threat Modeling
Activity 8
5.18 Challenges and Ethical Considerations in AI-Driven Penetration Testing
Activity 9
Activity 10
Quiz
Lab Practice 5.1 - VM Tool Based
Lab Practice 5.2 - Python Code Based
Python File Download
Module 6: Machine Learning for Threat Analysis
Module 6: Machine Learning for Threat Analysis
Audio Book: Machine Learning for Threat Analysis
6.1 Supervised Machine Learning Algorithms for Threat Detection
6.2 Boosting Security by Detecting and Mitigating Threats
Activity 1
6.3 Anomaly Detection
6.4 Common Techniques for Unsupervised Anomaly Detection
6.5 Evaluating Anomaly Detection Algorithms
6.6 Challenges and Limitations
Activity 2
6.7 Reinforcement Learning for Adaptive Security Measures
Activity 3
6.8 NLP Techniques for Threat Intelligence
Activity 4
6.9 Behavioral Analysis Using Machine Learning
6.10 Real-World Applications of Behavioral Analysis using Machine Learning
Activity 5
6.11 Ensemble Learning for Improved Threat Prediction
Activity 6
6.12 Feature Engineering in Threat Analysis
Activity 7
6.13 The Role of Machine Learning in Enhancing Endpoint Security
Activity 8
6.14 Explainable AI in Threat Analysis
Activity 9
Activity 10
Quiz
Lab Practice 6.1 - VM Tool Based
Lab Practice 6.2 - Python Code Based
Lab Practice 6.3 - Python Code Based
Python File Download
Module 7: Behavioral Analysis and Anomaly Detection for System Hacking
Module 7: Behavioral Analysis and Anomaly Detection for System Hacking
Audio Book: Behavioral Analysis and Anomaly Detection for System Hacking
7.1 Behavioral Biometrics for User Authentication: Overview
7.2 Behavioral Biometrics: Types, Advantages, and Limitations
Activity 1
7.3 Supervised Machine Learning Models for User Behavior Analysis: Regression Models
7.4 Types of Regression Models: Linear Regression
7.5 Types of Regression Models: Polynomial Regression
7.6 Types of Regression Models: Logistic Regression
7.7 Types of Regression Models: Ridge Regression
7.8 Types of Regression Models: Lasso Regression
7.9 Classification and the Importance of Classification Models in User Behavior Analysis
7.10 Use-Case: Detecting Suspicious Login Activities
7.11 Types of Classification Models: Random Forest
7.12 Types of Classification Models: Decision Trees
7.13 Types of Classification Models: SVM
7.14 Types of Classification Models: Neural Networks
7.15 Types of Classification Models: Naive Bayes
7.16 Unsupervised Machine Learning Models: Clustering
7.17 Types of Clustering: Centroid-Based
7.18 Types of Clustering: Density-Based Clustering
7.19 Types of Clustering: Distribution-Based Clustering
7.20 Types of Clustering: Hierarchical Clustering
7.21 Comparison of Clustering Methods
7.22 Dimensionality Reduction: Overview
7.23 Types of Dimension Reduction Techniques: Feature Selection
7.24 Types of Dimension Reduction Techniques: Dimensionality Reduction
7.25 Reinforcement Learning Models
Activity 2
7.26 Network Traffic Behavioral Analysis: Overview
7.27 NBA Systems: Components, Data, Learning, and Threat Detection
7.28 Techniques for Network Traffic Behavioral Analysis: Signature-based detection
7.29 Techniques for Network Traffic Behavioral Analysis: Behavioral Analysis
7.30 Techniques for Network Traffic Behavioral Analysis: Heuristic Analysis
7.31 Benefits of Network Traffic Behavioral Analysis
Activity 3
7.32 Endpoint Behavioral Monitoring
Activity 4
7.33 Time Series Analysis for Anomaly Detection: Overview
7.34 Time Series Analysis Techniques
7.35 Challenges in Time Series Anomaly Detection
Activity 5
7.36 Heuristic Approaches: Overview
7.37 Real-World Implications and Use-Cases of Heuristics
7.38 Key Heuristic Strategies, Techniques, and Their Applications
7.39 Advantages and Limitations of Heuristic Approaches
Activity 6
7.40 AI-driven Threat Hunting
7.41 Benefits of AI-driven Threat Hunting
Activity 7
7.42 User and Entity Behavior Analytics (UEBA)
7.43 Benefits of UEBA and Challenges in UEBA Deployment
Activity 8
7.44 Primary Challenges and Considerations
Activity 9
Quiz
Quiz
Lab Practice 7.1 - VM Tool Based
Lab Practice 7.2 - Python Code Based
Python File Download
Module 8: AI Enabled Incident Response Systems
Module 8: AI Enabled Incident Response Systems
Audio Book: AI Enabled Incident Response Systems
8.1 Automated Threat Triage: Overview
8.2 Core Processes and Tools Used in Automated Threat Triage
8.3 Significance in Modern Cybersecurity Defense
8.4 Benefits, Challenges, and Considerations of Automated Threat Triage Using AI
Activity 1
8.5 Understanding Threat Classification
8.6 Machine Learning Algorithms for Threat Classification
8.7 Feature Extraction Methods
8.8 Feature Extraction Techniques
8.9 Feature Selection Methods
8.10 Best Practices, Challenges, and Real-World Examples
8.11 Evaluation Metrics and Common Challenges in Threat Classification Models
8.12 Techniques to Improve Threat Classification Models
8.13 Real-World Model Deployment Considerations and Case Studies
8.14 Societal Implications and Strategies for Responsible Deployment and Decision-Making
Activity 2
8.15 Real-time Threat Intelligence Integration
8.16 Case Study: Integrating Real-Time Threat Intelligence with Ethical Hacking
8.17 Benefits, Approaches, and Best Practices for Real-time Threat Intelligence Integration
Activity 3
8.18 Predictive Analytics Techniques for Incident Response
8.19 Challenges, Limitations, and Future Directions in Predictive Analytics for Incident Response
8.20 Case Study: Leveraging Predictive Analytics for Incident Response in a Financial Institution
Activity 4
8.21 AI-Driven Incident Forensics and Benefits
8.22 AI Techniques in Incident Forensics
8.23 Case Study
Activity 5
8.24 Defining Automated Containment and Eradication
8.25 Components of Automated Containment and Eradication Strategies
8.26 Challenges and Limitations
Activity 6
8.27 Understanding Behavioral Analysis: Principles and Techniques for Analyzing Human Behavior
8.28 Working and Applications of Behavioral Analysis
8.29 Benefits of Behavioral Analysis in Incident Response
8.30 Process of Behavioral Analysis in Incident Response
8.31 Leveraging Behavior Patterns for Effective Cybersecurity Threat Management
8.32 Challenges of Behavioral Analysis in Incident Response
Activity 7
8.33 Continuous Improvement through Machine Learning Feedback
Activity 8
8.34 Human-AI Collaboration in Incident Handling
Activity 9
Quiz
Lab Practice 8.1 - VM Tool Based
Lab Practice 8.2 - Python Code Based
Lab Practice 8.3 - Python Code Based
Python File Download
Module 9: AI for Identity and Access Management (IAM)
Module 9: AI for Identity and Access Management (IAM)
Audio Book: AI for Identity and Access Management (IAM)
9.1 AI-Driven User Authentication Techniques
9.2 Voice Recognition
9.3 Behavioral Biometrics
9.4 Contextual Authentication
Activity 1
9.5 Understanding Behavioral Biometrics for Access Control
9.6 Types of Behavioral Biometrics
9.7 Advantages, Considerations, and Limitations of Behavioral Biometrics for Access Control
Activity 2
9.8 AI-Based Anomaly Detection in IAM
Activity 3
9.9 Introduction to Dynamic Access Policies
9.10 The Role of Machine Learning in Dynamic Access Policies
Activity 4
9.11 PAM: Overview and Key Concepts
9.12 AI-Enhanced PAM: Benefits, Challenges, and Considerations
Activity 5
9.13 Continuous Authentication using Machine Learning
Activity 6
9.14 Automated User Provisioning and De-provisioning: Overview, Benefits, and Challenges
9.15 Automated User Provisioning and De-provisioning: Key Components and Best Practices
Activity 7
9.16 Understanding Risk-Based Authentication and Its Benefits
9.17 AI in Risk-Based Authentication
Activity 8
9.18 IGA: Key Features and Working
9.19 AI-powered Identity Analytics
9.20 Intelligent Role Management
9.21 Intelligent Access Requests and Reviews
9.22 AI-Enhanced Access Certification
Activity 9
Activity 10
Quiz
Lab Practice 9.1 - Python Code Based
Lab Practice 9.2 - Python Code Based
Lab Practice 9.3 - Python Code Based
Lab Practice 9.4 - VM Tool Based
Lab Practice 9.5 - VM Tool Based
Lab Practice 9.6 - VM Tool Based
Python File Download
Module 10: Securing Ai Systems
Module 10: Securing AI Systems
Audio Book: Securing AI Systems
10.1 Adversarial Attacks on AI Models
Activity 1
10.2 Secure Model Training Practices
Activity 2
10.3 Data Privacy in AI Systems
Activity 3
10.4 Secure Deployment of AI Applications
Activity 4
10.5 AI model explainability and interpretability
Activity 5
10.6 Understanding Robustness in AI
10.7 Techniques for Robustness Enhancement
10.8 Resilience in AI
Activity 6
10.9 Secure Transfer of AI Models
10.10 Secure Sharing of AI Models
Activity 7
10.11 Monitoring AI Systems
10.12 Threat Detection for AI
Activity 8
Activity 9
Quiz
Lab Practice 10.1 - Python Code Based
Lab Practice 10.2 - Python Code Based
Lab Practice 10.3 - VM Tool Based
Lab Practice 10.4 - VM Tool Based
Python File Download
Module 11: Ethics in AI and Cybersecurity
Module 11: Ethics in AI and Cybersecurity
Audio Book: Ethics in AI and Cybersecurity
11.1 Ethical Guidelines in Cybersecurity
11.2 Ethical Decision-Making Models: The Consequentialist Model
11.3 Deontological (Duty-Based) Model
11.4 The Rights-Based Model
11.5 The Ethical Decision-Making Process Model
11.6 Ethical Considerations in Cybersecurity
Activity 1
11.7 Understanding Bias in AI Algorithms
11.8 Impact of Bias in AI Algorithms
11.9 Addressing Bias in AI Algorithms
Activity 2
11.10 Understanding Transparency in AI Systems
11.11 The Need for Explainability in AI Systems
11.12 Frameworks for Achieving Transparency and Explainability
Activity 3
11.13 Privacy Concerns in AI-Driven Cybersecurity
Activity 4
11.14 Legal and Ethical Aspects of AI Security
11.15 Ethics of Threat Intelligence Sharing
Activity 5
11.16 Human Rights and AI in Cybersecurity
Activity 6
11.17 Regulatory Compliance and Ethical Standards
Activity 7
11.18 Ethical Hacking and Responsible Disclosure
Activity 8
Activity 9
Quiz
Module 12: Capstone Project
Module 12: Capstone Project
Audio Book: Capstone Project
12.1 Case Study 1: AI-Enhanced Threat Detection and Response
12.2 Case Study 2: Ethical Hacking with AI Integration
12.3 Case Study 3: AI in Identity and Access Management (IAM)
12.4 Case Study 4: Secure Deployment of AI Systems
12.5 Capstone Projects
Quiz
Capstone Project 1 - (Python Code Based)
Capstone Project 2 - (Python Code Based)
Capstone Project 3 - (Python Code Based)
Python File Download
Course Summary
Course Summary
Additional Module - AI Agents For Ethical Hacker
AI Agents Ethical Hacker
Resources
AI+ Ethical Hacker Blueprint
AI+ Ethical Hacker Detailed Curriculum
AI+ Ethical Hacker Tools
AI+ Ethical Hacker Resources and References
AI CERTs Exam Guidelines
AI CERTs Exam Guidelines
AI+ Ethical Hacker Examination
AI+ Ethical Hacker Examination
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AI+ Ethical Hacker™ Self Paced Learning V3
Skill Level
:
Beginner
certificate name
:
AI+ Ethical Hacker™
Badge
:
Badge_AI+-Ethical-Hacker.png
Course Layout
:
Standard
