AI Security

AI+ Security Level 2

This course advances AI-driven cybersecurity expertise, starting with Python fundamentals. Participants apply machine learning to detect threats, implement AI authentication, and explore GANs for security. Hands-on learning, including a Capstone Project, ensures practical skills in protecting digital assets.

Duration

5 Days (40 hours)

Targeted Audience

Threat Intelligence Specialist

Security Specialist

Cybersecurity Analyst

Data Security Engineer

Prerequisites for Success

Completion of AI+ Security Level 1™, but 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.

Modules | 10

Examination | 1

Passing Score | 70%

COURSE AGENDA

Module 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

Module 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

Module 3: Application 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 10: Capstone Project

10.1 Introduction
10.2 Use Cases: AI in Cybersecurity
10.3 Outcome Presentation