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AI+ Security Level 1™ Spanish
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Introduction
Course Introduction
Module 1: Introduction to AI Cybersecurity
1.1 Overview of Cybersecurity and Its Domains
1.2 Scope and Objectives
1.3 Threats, Vulnerabilities, and Risks
1.4 Defense in Depth
1.5 Security Policies and Procedures
1.6 Access Control and Cryptography
1.7 CIA Triad (Confidentiality, Integrity, Availability)
1.8 Cybersecurity Frameworks: NIST and ISO/IEC 27001
1.9 Comparison of Frameworks and Implementation Guidelines
1.10 General Data Protection Regulation (GDPR)
1.11 Health Insurance Portability and Accountability Act (HIPAA)
1.12 Compliance Strategies and Legal Consequences
1.13 Business Continuity
1.14 Regulatory Compliance, Innovation, and Competitive Advantage
1.15 Careers in Cyber Security
Module 2: Operating System Fundamentals
2.1 Memory and Process Management
2.2 File System and Device Management
2.3 System Performance Monitoring
2.4 User Account Types and Account Management
2.5 Privilege Levels, Authentication Mechanisms, and Account Security Best Practices
2.6 ACLs, DAC, MAC, RBAC, and ABAC
2.7 OS Security Features and Configurations
2.8 Patching and Updates
2.9 Service Management and Security Policies
2.10 Configuration Management and Security Audits
2.11 Virtual Machine and Container Security
2.12 Hypervisor Security
2.13 Network Isolation
2.14 Security Tools
2.15 Secure Boot
2.16 Trusted Platform Module (TPM)
2.17 Remote Access Protocols
2.18 Multi-Factor Authentication (MFA)
2.19 Remote Access Best Practices
2.20 Common OS Vulnerabilities
2.21 Vulnerability Assessment Tools
2.22 Patch Management
2.23 Security Hardening
2.24 Incident Response
Module 3: Networking Fundamentals
3.1 Network Topologies
3.2 Transmission Control Protocol/Internet Protocol (TCP/IP) Model
3.3 Open Systems Interconnection (OSI) Model
3.4 Protocol Overview and Functionality
3.5 Routers and Switches
3.6 Firewalls, Access Points, and Network Interface Cards (NICs)
3.7 Case Study
3.8 Firewalls and Intrusion Detection Systems (IDS)
3.9 Intrusion Prevention Systems (IPS)
3.10 Unified Threat Management (UTM) and Network Access Control (NAC)
3.11 Case Study
3.12 Network Segmentation
3.13 VLANs and DMZ (Demilitarized Zone)
3.14 Subnetting and Security Zones
3.15 Wireless Security Protocols
3.16 WEP Vulnerabilities and WPA2 Security
3.17 Case Study
3.18 VPN Technologies and Use Cases
3.19 NAT Overview, Types, and Configuration
3.20 Basic Network Troubleshooting
Module 4: Threats, Vulnerabilities and Exploits
4.1 Script Kiddies, Hacktivists, and Nation-State Actors
4.2 Cyber Criminals and Insider Threats
4.3 Threat Hunting Methodologies using AI
4.4 SIEM, IDS, and IPS
4.5 Endpoint Detection and Response (EDR) and User and Entity Behavior Analytics (UEBA)
4.6 Exploring OSINT: Data Sources and Tools
4.7 OSINT Techniques, Strategies and Ethical Considerations
4.8 Vulnerabilities: Types and Common Examples
4.9 Vulnerability Lifecycle and Databases
4.10 DevSecOps and Secure Coding Practices
4.11 Zero-Day Attacks: Detection and Prevention
4.12 Patch Management: Automation and Validation
4.13 Vulnerability Scanning: AI and Automation
4.14 Risk Assessment and Remediation Strategies
4.15 Exploit Development
4.16 Metasploit Framework
4.17 Buffer Overflow Exploits
4.18 Web Application Exploits
4.19 Post-Exploitation Techniques
Module 5: Understanding of AI and ML
5.1 Definition and History of AI
5.2 Supervised, Unsupervised, and Reinforcement Learning
5.3 Natural Language Processing (NLP)
5.4 Computer Vision
5.5 Risk Assessment, Bias, Fairness, and Data Privacy
5.6 AI-Driven Threat Detection and Anomaly Detection
5.7 Adaptive Defense Mechanisms and AI for Incident Response
5.8 Case Studies
5.9 Cybersecurity AI and Threat Intelligence
5.10 Predictive Analytics, AI-Driven Automation, and Continuous Learning
5.11 Application in Intrusion Detection
5.12 Malware Detection and Phishing Detection
5.13 Case Study
Module 6: Python Programming Fundamentals
6.1 Python Basics: Control Structures and Functions
6.2 Error Handling
6.3 Basic Input/Output
6.4 Understanding of Python Libraries
6.5 Socket Programming
6.6 Cryptography
6.7 Parsing Network Traffic
6.8 Log Analysis
6.9 Web Scraping
6.10 Automating Tasks with Python and Integration with Security Tools
6.11 Incident Response Automation and Custom Security Tools
6.12 Data Import, Export, Cleaning and Transformation
6.13 Statistical Analysis and Visualization
6.14 Custom Scanners
6.15 Brute Force Tools
6.16 Keyloggers
6.17 Forensics Tools
6.18 Malware Analysis
Module 7: Applications of AI in Cybersecurity
7.1 Understanding the Application of ML in Cybersecurity
7.2 Anomaly Detection Techniques
7.3 Behavior Analysis and Real-Time Monitoring
7.4 Case Studies, Tools and Frameworks
7.5 Dynamic and Proactive Defense using ML
7.6 Utilizing ML for Email Threat Detection
7.7 Case Studies
7.8 Phishing Detection
7.9 Education, Automation, and Simulation
7.10 Email Threat Landscape and AI Algorithms for Threat Identification
7.11 Automated Response Mechanisms and Continuous Learning
7.12 Case Studies
7.13 Employing Advanced Algorithms and AI in Malware Threat Detection
7.14 Case Studies
7.15 Identifying, Analyzing, and Mitigating Malicious Software
7.16 Automated Remediation and Case Studies
7.17 Enhancing User Authentication with AI Techniques
7.18 Case Studies
7.19 Penetration Testing with A
7.20 Case Studies
Module 8: Incident Response and Disaster Recovery
8.1 Incident Response Process (Identification, Containment, Eradication, Recovery
8.2 Incident Response Lifecycle
8.3 Developing the Plan and Defining Roles
8.4 Communication Protocols
8.5 Resource Allocation, Testing, and Drills
8.6 Incident Detection Tools and Analysis Techniques
8.7 Incident Classification and Evidence Collection
8.8 Post-Incident Activities
8.9 Digital Forensics and Evidence Collection Techniques
8.10 Chain of Custody
8.11 Forensic Tools, Techniques, and Legal Considerations
8.12 Disaster Recovery Planning
8.13 Backup Strategies
8.14 Business Continuity Planning
8.15 Testing and Drills
8.16 Plan Maintenance
8.17 Penetration Testing and Vulnerability Assessments
8.18 Legal and Regulatory Considerations of Security Incidents
Module 9: Open-Source Security Tools
9.1 Introduction to Open-Source Security Tools
9.2 Wireshark
9.3 Nmap
9.4 Snort
9.5 Metasploit
9.6 OSSEC
9.7 Implementing Open-Source Solutions in Organizations
9.8 Case Studies
9.9 Community Support and Resources
9.10 Nmap, OpenVAS, and Nessus
9.11 Nikto and VulnHub
9.12 SIEM Tools (Open-Source options)
9.13 Open-Source Packet Filtering Firewalls
9.14 Hashcat and John the Ripper
9.15 Cain and Abel
9.16 Hydra and Ophcrack
9.17 Autopsy and Sleuth Kit
9.18 Volatility, Plaso, and X1 Search
Module 10: Securing the Future
10.1 Emerging Cyber Threats and Trends
10.2 Blockchain in Cybersecurity
10.3 Real-World Examples
10.4 IoT and Network Security
10.5 Case Studies and Best Practices
10.6 Cloud Computing and Security Risks
10.7 Case Studies and Best Practices
10.8 Quantum Computing and Threats to Cryptography
10.9 Case Studies and Best Practices
10.10 Cryptography Fundamentals
10.11 Symmetric and Asymmetric Encryption
10.12 Secure Hash Functions and Cryptographic Protocol
10.13 Cybersecurity Awareness and Training for Users
10.14 Continuous Security Monitoring and Improvement
Module 11: Capstone Project
11.1 Capstone Project: Scope, Project Phases, and Outcome Presentation
11.2 Use Cases: AI in Cybersecurity
Course Summary
Course Summary
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AI+ Security Level 1™ Spanish
