Recommended Prerequisites:
- Completion of AI+ Security Level 1, not mandatory
- Basic Python Skills: Familiarity with Python basics, including variables, loops, and functions
- Basic Cybersecurity: Basic understanding of cybersecurity principles, such as the CIA triad and common cyber threats
- Basic Machine Learning Awareness: General awareness about machine learning, no technical skills required
- Basic Networking Knowledge: Understanding of IP addresses and how the internet works.
- Basic Command Line Skills: Comfort using the command line like Linux or Windows terminal for basic tasks
- Interest in AI for Security: Willingness to explore how AI can be applied to detect and mitigate security threats.
Course Outline:
Lesson 1: Introduction to Artificial Intelligence (AI) and Cyber Security
- 1.1 Understanding the Cyber Security Artificial Intelligence (CSAI)
- 1.2 An Introduction to AI and its Applications in Cybersecurity
- 1.3 Overview of Cybersecurity Fundamentals
- 1.4 Identifying and Mitigating Risks in Real-Life
- 1.5 Building a Resilient and Adaptive Security Infrastructure
- 1.6 Enhancing Digital Defenses using CSAI
Lesson 2: Python Programming for AI and Cybersecurity Professionals
- 2.1 Python Programming Language and its Relevance in Cybersecurity
- 2.2 Python Programming Language and Cybersecurity Applications
- 2.3 AI Scripting for Automation in Cybersecurity Tasks
- 2.4 Data Analysis and Manipulation Using Python
- 2.5 Developing Security Tools with Python
Lesson 3: Applications of Machine Learning in Cybersecurity
- 3.1 Understanding the Application of Machine Learning in Cybersecurity
- 3.2 Anomaly Detection to Behaviour Analysis
- 3.3 Dynamic and Proactive Defense using Machine Learning
- 3.4 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats
Lesson 4: Detection of Email Threats with AI
- 4.1 Utilizing Machine Learning for Email Threat Detection
- 4.2 Analyzing Patterns and Flagging Malicious Content
- 4.3 Enhancing Phishing Detection with AI
- 4.4 Autonomous Identification and Thwarting of Email Threats
- 4.5 Tools and Technology for Implementing AI in Email Security
Lesson 5: AI Algorithm for Malware Threat Detection
- 5.1 Introduction to AI Algorithm for Malware Threat Detection
- 5.2 Employing Advanced Algorithms and AI in Malware Threat Detection
- 5.3 Identifying, Analyzing, and Mitigating Malicious Software
- 5.4 Safeguarding Systems, Networks, and Data in Real-time
- 5.5 Bolstering Cybersecurity Measures Against Malware Threats
- 5.6 Tools and Technology: Python, Malware Analysis Tools
Lesson 6: Network Anomaly Detection using AI
- 6.1 Utilizing Machine Learning to Identify Unusual Patterns in Network Traffic
- 6.2 Enhancing Cybersecurity and Fortifying Network Defenses with AI Techniques
- 6.3 Implementing Network Anomaly Detection Techniques
Lesson 7: User Authentication Security with AI
- 7.1 Introduction
- 7.2 Enhancing User Authentication with AI Techniques
- 7.3 Introducing Biometric Recognition, Anomaly Detection, and Behavioural Analysis
- 7.4 Providing a Robust Defence Against Unauthorized Access
- 7.5 Ensuring a Seamless Yet Secure User Experience
- 7.6 Tools and Technology: AI-based Authentication Platforms
- 7.7 Conclusion
Lesson 8: Generative Adversarial Network (GAN) for Cyber Security
- 8.1 Introduction to Generative Adversarial Networks (GANs) in Cybersecurity
- 8.2 Creating Realistic Mock Threats to Fortify Systems
- 8.3 Detecting Vulnerabilities and Refining Security Measures Using GANs
- 8.4 Tools and Technology: Python and GAN Frameworks
Lesson 9: Penetration Testing with Artificial Intelligence
- 9.1 Enhancing Efficiency in Identifying Vulnerabilities Using AI
- 9.2 Automating Threat Detection and Adapting to Evolving Attack Patterns
- 9.3 Strengthening Organizations Against Cyber Threats Using AI-driven Penetration Testing
- 9.4 Tools and Technology: Penetration Testing Tools, AI-based Vulnerability Scanners
Lesson 10: Capstone Project
- 10.1 Introduction
- 10.2 Use Cases: AI in Cybersecurity
- 10.3 Outcome Presentation
