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AI+ Ethical Hacker™ Spanish
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Introduction
Course Introduction
Module 1: Foundation of Ethical Hacking using Artificial Intelligence (AI)
1.1 Ethical Hacking: Roles and Responsibilities
1.2 Ethical Hacking: Knowledge, Skills, Tools and Techniques
1.3 Ethical Hacking Methodology
1.4 Legal and Regulatory Framework
1.5 Hacker Types and Motivations
1.6 Footprinting and Reconnaissance
1.7 Types of Network Scanning
1.8 Common Scanning Tools
1.9 Enumeration: Importance, Distinction, and Process
1.10 Port Scanning and Comprehensive Enumeration Techniques
Module-2: 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 Sentiment Analysis
2.6 Computer Vision: A Detailed Exploration with Case Studies
2.7 Deep Learning
2.8 Reinforcement Learning
2.9 Machine Learning in Cybersecurity
2.10 Natural Language Processing (NLP) for Cybersecurity
2.11 Deep Learning for Threat Detection
2.12 Adversarial Attacks: Mitigation, Challenges, and Future Directions
2.13 AI-Driven Threat Intelligence Platforms
2.14 Case Studies and Future Trends
2.15 Cybersecurity Automation with AI
Module-3: AI Tools and Technologies in Ethical Hacking
3.1 AI-based Threat Detection Tools
3.2 Popular Machine Learning Frameworks
3.3 AI-Enhanced Penetration Testing Tools
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
3.7 Importance and Key Features of AI-Driven Network Security Solutions
3.8 Automated Vulnerability Scanners and Types
3.9 Key Features, Benefits, and Limitations of Automated Vulnerability Scanners
3.10 Popular Automated Vulnerability Scanners
3.11 AI in Web Application
3.12 AI for Malware Detection and Analysis
3.13 Cognitive Security Tools
Module 4: AI-Driven Reconnaissance Techniques
4.1 Reconnaissance: Types, Methods, and Tools
4.2 Traditional vs. Ai-Driven Reconnaissance
4.3 OS Fingerprinting: Importance and Traditional Techniques
4.4 AI-Powered OS Fingerprinting Techniques
4.5 Various AI-powered Port Scanning Techniques
4.6 Machine Learning for Network Mapping
4.7 AI-Driven Social Engineering Reconnaissance
4.8 Machine Learning in OSINT
4.9 AI-Enhanced DNS Enumeration and AI-Driven Target Profiling
Module 5: AI in Vulnerability Assessment and Penetration Testing
5.1 Automated Vulnerability Scanning with AI
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
5.7 Fundamentals of Machine Learning
5.8 Exploitation Techniques
5.9 Evaluation and Limitations of ML-Based Exploitation Techniques
5.10 Dynamic Application Security Testing (DAST): Overview
5.11 Applications and Benefits of AI in DAST
5.12 Fuzz Testing: A Brief Overview
5.13 AI-Driven Fuzz Testing: How It Works
5.14 Adversarial Machine Learning Techniques
5.15 Evaluating Security Systems Using Adversarial Machine Learning
5.16 Automated Report Generation using AI
5.17 AI-Based Threat Modeling
5.18 Challenges and Ethical Considerations in AI-Driven Penetration Testing
Module 6: Machine Learning for Threat Analysis
6.1 Supervised Machine Learning Algorithms for Threat Detection
6.2 Boosting Security by Detecting and Mitigating Threats
6.3 Anomaly Detection
6.4 Common Techniques for Unsupervised Anomaly Detection
6.5 Evaluating Anomaly Detection Algorithms
6.6 Challenges and Limitations
6.7 Reinforcement Learning for Adaptive Security Measures
6.8 NLP Techniques for Threat Intelligence
6.9 Behavioral Analysis Using Machine Learning
6.10 Real-World Applications of Behavioral Analysis using Machine Learning
6.11 Ensemble Learning for Improved Threat Prediction
6.12 Feature Engineering in Threat Analysis
6.13 The Role of Machine Learning in Enhancing Endpoint Security
6.14 Explainable AI in Threat Analysis
Module 7: Behavioral Analysis and Anomaly Detection for System Hacking
7.1 Behavioral Biometrics for User Authentication: Overview
7.2 Behavioral Biometrics: Types, Advantages, and Limitations
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
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
7.32 Endpoint Behavioral Monitoring
7.33 Time Series Analysis for Anomaly Detection: Overview
7.34 Time Series Analysis Techniques
7.35 Challenges in Time Series Anomaly Detection
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
7.40 AI-driven Threat Hunting
7.41 Benefits of AI-driven Threat Hunting
7.42 User and Entity Behavior Analytics (UEBA)
7.43 Benefits of UEBA and Challenges in UEBA Deployment
7.44 Primary Challenges and Considerations
Module 8: 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
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
8.15 Real-time Threat Intelligence Integration
8.16 Integrating Real-Time Threat Intelligence with Ethical Hacking
8.17 Benefits, Approaches, and Best Practices for Real-time Threat Intelligence Integration
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
8.21 AI-Driven Incident Forensics and Benefits
8.22 AI Techniques in Incident Forensics
8.23 Case Study
8.24 Defining Automated Containment and Eradication
8.25 Components of Automated Containment and Eradication Strategies
8.26 Challenges and Limitations
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
8.33 Continuous Improvement through Machine Learning Feedback
8.34 Human-AI Collaboration in Incident Handling
Module-9: 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
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
9.8 AI-Based Anomaly Detection in IAM
9.9 Introduction to Dynamic Access Policies
9.10 The Role of Machine Learning in Dynamic Access Policies
9.11 PAM: Overview and Key Concepts
9.12 AI-Enhanced PAM: Benefits, Challenges, and Considerations
9.13 Continuous Authentication using Machine Learning
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
9.16 Understanding Risk-Based Authentication and It's Benefits
9.17 AI in Risk-Based Authentication
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
Module-10: Securing Ai Systems
10.1 Adversarial Attacks on AI Models
10.2 Secure Model Training Practices
10.3 Data Privacy in AI Systems
10.4 Secure Deployment of AI Applications
10.5 AI model explainability and interpretability
10.6 Understanding robustness in AI
10.7 Techniques for robustness enhancement
10.8 Resilience in AI
10.9 Secure Transfer of AI models
10.10 Secure sharing of AI models
10.11 Monitoring AI systems
10.12 Threat detection for AI
Module-11: 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
11.7 Understanding Bias in AI Algorithms
11.8 Impact of Bias in AI Algorithms
11.9 Addressing Bias in AI Algorithms
11.10 Understanding Transparency in AI Systems
11.11 The Need for Explainability in AI Systems
11.12 Frameworks for Achieving Transparency and Explainability
11.13 Privacy Concerns in AI-Driven Cybersecurity
11.14 Legal and Ethical Aspects of AI Security
11.15 Ethics of Threat Intelligence Sharing
11.16 Human Rights and AI in Cybersecurity
11.17 Regulatory Compliance and Ethical Standards
11.18 Ethical Hacking and Responsible Disclosure
Module 12: 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
Course Summary
Course Summary
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AI+ Ethical Hacker™ Spanish
