Course Outline
Module 1: Introduction to Artificial Intelligence (AI) and Cyber Security (8%)
• Understanding the Cyber Security Artificial Intelligence (CSAI)
• An Introduction to AI and its Applications in Cybersecurity
• Overview of Cybersecurity Fundamentals
• Identifying and Mitigating Risks in Real-Life
• Building a Resilient and Adaptive Security Infrastructure
• Enhancing Digital Defenses using CSAI
Module 2: Python Programming for AI and Cybersecurity Professionals (10%)
• 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 (10%)
• 3.1 Understanding the Application of Machine Learning in Cybersecurity
• 3.2 Anomaly Detection to Behavior 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 (11%)
• 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 (11%)
• 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 (11%)
• 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 (11%)
• 7.1 Introduction
• 7.2 Enhancing User Authentication with AI Techniques
• 7.3 Introducing Biometric Recognition, Anomaly Detection, and Behavioral Analysis
• 7.4 Providing a Robust Defense Against Unauthorized Access
• 7.5 Ensuring a Seamless Yet Secure User Experience
• 7.6 Tools and Technology: AI-based Authentication
• 7.7 Conclusion
Module 8: Generative Adversarial Network (GAN) for Cyber Security (11%)
• 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 (11%)
• 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 (6%)
• 10.1 Introduction
• 10.2 Use Cases: AI in Cybersecurity
• 10.3 Outcome Presentation
