AI+ Security Level 1 (AISEC) Online

Voraussetzungen

• Basic Python Programming: Familiarity with loops, functions, and variables.
• Basic Cybersecurity Knowledge: Understanding of CIA triad and common threats (e.g., malware, phishing).
• Basic Machine Learning Concepts: Awareness of fundamental machine learning concepts, not mandatory.
• Basic Networking: Understanding of IP addressing and TCP/IP protocols.
• Linux/Command Line Skills: Ability to navigate and use the CLI effectively.

Zielgruppe

• Cybersecurity Professionals and Analysts
• Penetration Testers
• Security Consultants
• Incident Responders
• Security Engineers
• Threat Hunters
• Compliance Auditors
• Network Security Administrators
• Forensic Analysts
• IT Professionals and System Administrators
• Risk Management Specialists
• Business Leaders and Decision Makers
• Software Developers

Detaillierter Kursinhalt

Module 1: Introduction to Cybersecurity

• 1.1 Definition and Scope of Cybersecurity
• 1.2 Key Cybersecurity Concepts
• 1.3 CIA Triad (Confidentiality, Integrity, Availability)
• 1.4 Cybersecurity Frameworks and Standards (NIST, ISO/IEC 27001)
• 1.5 Cyber Security Laws and Regulations (e.g., GDPR, HIPAA)
• 1.6 Importance of Cybersecurity in Modern Enterprises
• 1.7 Careers in Cyber Security

Module 2: Operating System Fundamentals

• 2.1 Core OS Functions (Memory Management, Process Management)
• 2.2 User Accounts and Privileges
• 2.3 Access Control Mechanisms (ACLs, DAC, MAC)
• 2.4 OS Security Features and Configurations
• 2.5 Hardening OS Security (Patching, Disabling Unnecessary Services)
• 2.6 Virtualization and Containerization Security Considerations
• 2.7 Secure Boot and Secure Remote Access
• 2.8 OS Vulnerabilities and Mitigations

Module 3: Networking Fundamentals

• 3.1 Network Topologies and Protocols (TCP/IP, OSI Model)
• 3.2 Network Devices and Their Roles (Routers, Switches, Firewalls)
• 3.3 Network Security Devices (Firewalls, IDS.IPS)
• 3.4 Network Segmentation and Zoning
• 3.5 Wireless Network Security (WPA2, Open WEP vulnerabilities)
• 3.6 VPN Technologies and Use Cases
• 3.7 Network Address Translation (NAT)
• 3.8 Basic Network Troubleshooting

Module 4: Threats, Vulnerabilities, and Exploits

• 4.1 Types of Threat Actors (Script Kiddies, Hacktivists, Nation-States)
• 4..2 Threat Hunting Methodologies using AI
• 4.3 AI Tools for Threat Hunting (SIEM, IDS/IPS)
• 4.4 Open-Source Intelligence (OSINT) Techniques
• 4.5 Introduction to Vulnerabilities
• 4.6 Software Development Life Cycle (SDLC) and Security Integration with AI
• 4.7 Zero-Day Attacks and Patch Management Strategies
• 4.8 Vulnerability Scanning Tools and Techniques using AI
• 4.9 Exploiting Vulnerabilities (Hands-on Labs)

Module 5: Understanding of AI and ML

• 5.1 An Introduction to AI
• 5.2 Types of Applications of AI
• 5.3 Identifying and Mitigating Risks in Real-Life
• 5.4 Building a Resilient and Adaptive Security Infrastructure with AI
• 5.5 Enhancing Digital Defenses using CSAI
• 5.6 Application of Machine Learning in Cybersecurity
• 5.7 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats
• 5.8 Threat Intelligence and Threat Hunting Concepts

Module 6: Python Programming Fundamentals

• 6.1 Introduction to Python Programming
• 6.2 Understanding of Python Libraries
• 6.3 Python Programming Language for Cybersecurity Applications
• 6.4 AI Scripting for Automation in Cybersecurity Tasks
• 6.5 Data Analysis and Manipulation Using Python
• 6.6 Developing Security Tools with Python

Module 7: Applications of AI in Cybersecurity

• 7.1 Understanding the Application of Machine Learning in Cybersecurity
• 7.2 Anomaly Detection to Behavior Analysis
• 7.3 Dynamic and Proactive Defense using Machine Learning
• 7.4 Utilizing Machine Learning for Email Threat Detection
• 7.5 Enhancing Phishing Detection with AI
• 7.6 Autonomous Identification and Thwarting of Email Threats
• 7.7 Employing Advanced Algorithms and AI in Malware Threat Detection
• 7.8 Identifying, Analyzing, and Mitigating Malicious Software
• 7.9 Enhancing User Authentication with AI Techniques
• 7.10 Penetration Testing with AI

Module 8: Incident Response and Disaster Recovery

• 8.1 Incident Response Process (Identification, Containment, Eradication, Recovery)
• 8.2 Incident Response Lifecycle
• 8.3 Preparing an Incident Response Plan
• 8.4 Detecting and Analyzing Incidents
• 8.5 Containment, Eradication, and Recovery
• 8.6 Post-Incident Activities
• 8.7 Digital Forensics and Evidence Collection
• 8.8 Disaster Recovery Planning (Backups, Business Continuity)
• 8.9 Penetration Testing and Vulnerability Assessments
• 8.10 Legal and Regulatory Considerations of Security Incidents

Module 9: Open Source Security Tools

• 9.1 Introduction to Open-Source Security Tools
• 9.2 Popular Open Source Security Tools
• 9.3 Benefits and Challenges of Using Open-Source Tools
• 9.4 Implementing Open Source Solution in Organizations
• 9.5 Community Support Resources
• 9.6 Network Security Scanning and Vulnerability Detection
• 9.7 Security Information and Event Management (SIEM) Tools (Open-Source options)
• 9.8 Open-Source Packet Filtering Firewalls
• 9.9 Password Hashing and Cracking Tools (Ethical Use)
• 9.10 Open-Source Forensics Tools

Module 10: Securing the Future

• 10.1 Emerging Cyber Threats and Trends
• 10.2 Artificial Intelligence and Machine Learning in Cybersecurity
• 10.3 Blockchain for Security
• 10.4 Internet of Things (IoT) Security
• 10.5 Cloud Security
• 10.6 Quantum Computing and its Impact on Security
• 10.7 Cybersecurity in Critical Infrastructure
• 10.8 Cryptography and Secure Hashing
• 10.9 Cyber Security Awareness and Training for Users
• 10.10 Continuous Security Monitoring and Improvement

Capstone Project

• 11.1 Introduction
• 11.2 Use Cases: AI in Cybersecurity
• 11.3 Outcome Presentation